This is Part 4 for the project focused on the extension and multiverse analysis of a publication in Development and Psychopathology 2020
The sections are split across Aim 1 and Aim 2. In Aim 1 we conduct the replication analyses from the original study, which focused on the mediating role of puberty (parent report) on the direct association between the Family Environment factor (independent variable) and the brain outcomes (5 brain outcomes: Amygdala volume, ACC thickness, ACC area, and Left & Right Amyg-Cing Network resting state connectivity. Note, we do not use the sixth brain outcome, ACC FA, due to the significant change in preprocessing of DWI data between release 1 and 2). In Aim 2 we extend these results by conducting the multiverse analyses for model permutations across IV variables and the mediator, with all brain outcomes unchanged.
Note: the option for this rmarkdown is
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if (!require("pacman")) install.packages("pacman")
pacman::p_load(janitor, cowplot, dplyr, readr, devtools, table1, MatchIt, kableExtra, corrplot, multiplex, MplusAutomation,lavaanPlot, semPlot, lavaan, parameters, specr, MASS, Hmisc, patchwork)At each instance, load packages.
# Load packages
library(tidyverse) # piping, descriptives, etc
library(kableExtra) # for table formatting
library(lavaan) # running lavaan Factor models + mediation
library(lavaanPlot)
library(parameters) # reporting lavaan parameters in table
library(specr)
library(cowplot)
library(MASS)
library(glue)
library(corrplot)
library(Hmisc)
library(patchwork)Here we import the data that was created during Part 2 & 3, which includes our variables and factor scores being used in the models below.
data <- read.csv("../../stage1/ABCD_wFactorScores_2022_07_11.csv")minor correction, the preregistration missed the step
which averaged the parent/child reported PDS score & the z-scored
parental income and education variable. So we create these variables
below.
# averaging the parent and child reported PDS scores
data <- data %>%
mutate(avg_puberty = rowMeans(dplyr::select(.,c(p_puberty,y_puberty)),
na.rm = F)) # Use F instead of T, this way NA is provided in cases of missing values on yth or par report.
# z-scoring & averaging parental income and education
data$Par_Inc_z <- scale(data$Par_Inc, center = T, scale = T)
data$Par_Edu_z <- scale(data$Par_Edu, center = T, scale = T)
data <- data %>%
mutate(Avg_IncomeEduc = rowMeans(dplyr::select(.,c(Par_Inc_z,Par_Edu_z)),
na.rm = F))Let’s also rename the variables that we use so they are easily interpretable and easier to use the numerical values the models below.
data <- data %>%
rename(
# IV: renaming our factor scores
FamEnv_Fact = FamEnv_Score_r, Demo_Fact = Demo_Score_r,
Par_Fact = Par_Score_r, Child_Fact = Child_Score,
# IV: renaming variables
FES_youth = FES_y_mean_r, CRPBI = CRPBI_mean, PMON = PMON_y_mean,
FES_parent = FES_p_mean_r,
# outcome brain
Amygdala_vol = Amyg_Vol_z, ACC_CA = ACC_Area_z,
ACC_CT = ACC_Thick_z,
L_AmygCing_rest = Lamyg_CingOperc, R_AmygCing_rest = Ramyg_CingOperc,
# covariates
)
data = data %>%
mutate(race_r = case_when(race == 'White' ~ 1,
race == 'Black' ~ 2,
race == 'Hispanic' ~ 3,
race == 'Asian' ~ 4,
race == 'Other' ~ 5),
sex_r = case_when(sex == 'F' ~ 0,
sex == 'M' ~ 1)
)Consistent with the preregistered table 1 the variables and their associated variable names that we will focus on for the replication^ and multiverse analyses are as follows
Indepdendent variables (IV)
Mediating Variables (M)
Outcome Variables (DV)
Covariates in all models
For purposes of coherence in abbreviations as we report in the manuscript, we removed underscores (e.g., FamEnv_Fact –> FamEnv Fact) and renamed some variables as follows. FES_youth = FES Yth; FES_parent = FES Par; CRPBI = Par Accept; Avg IncomeEduc = Avg IncEdu; p_puberty = PDS Par; y_puberty = PDS Par; avg_puberty = PDS ParYth; L/R AmygCing_rest = L/R AmygCON.
Here we define our multiverse function, where model inputs include:
five models
*Note: Fam | Par Puberty | ACC FA we do not include the
FA measure given the changes in preprocessing for DWI data between the
releases which impacted the underlying estimates of FA.
Some helpful notes regarding this function. There are three steps.
The function requires input of: X = list of independent
variables, Y = list of dependent variables, M
= list of mediators, Cov = list of covariates (note, for
this project we do not have a list, but in stead use constants of Age,
Race, Sex), df A datamrame that contains our variables
(columes) and participants (rows), lavaan_model = this is
the syntax specifying the mediation model, model_boot = the
number of bootstraps to run in the mediation model that calculates
significance for our effects.
Step 1 simply takes in the X, Y, M list + Cov1, Cov2, Cov2 constants and creates the permutation of variable combinations. This is simply the product of the list of items in X, Y, M, Covs. It calculates this product and spits out the combinations used.
In step 2 we use apply()
to each row to run all variable permutations (matrix rows) for our
lavaan::sem() model. The apply function takes each row of variables
(stored in var_perumtations), sends them into the function
variable combos, subsets our df by these
variables and creates these column names w/ that data. On this data,
lavaan function is used to fit the specified model. The mediation model
uses the permutations of X, Y, M + constant covariates.
After Step 2 is complete, we will get a list of
total permutations saved to the data_rep. We
can access the model, variable and fit statistics via different ways.
Here, we use lappy() to loop through this list, and send each instance
to function(x) where x is each iteration of output in
data_rep. We use the parameters package to
save the standardized model parameters to a data.frame, and we save the
variable names for each model Using the location of each variable and
parameter name and estimate value, we extract this estimate and save it
to a data.frame. The variables that are relevant here are:
We combine across rows to get these values in a signal dataframe, then relabel the column names so we have the output structure that is comparable to the specr() output style to use w/ plot_spercs() function(s).
in the pre-registered code there was an error in which estimates
were extract. In the pre-registration model_parameters(fit, standardize
= FALSE) when it should have been model_parameters(fit,
standardize = TRUE)
mediation_specr <- function(X, Y, M, Cov1, Cov2, Cov3, df, lavaan_model, model_boot) {
## Step 1
# Creating variable permutations for lists in X, Y, M, Cov
var_permutations <- as.matrix(expand.grid(X = X,
Y = Y,
M = M,
Age = Cov1,
Race = Cov2,
Sex = Cov3))
# Check the number of permutations that are created basic ont he input strings
total_permutes <- length(X)*length(Y)*length(M)*length(Cov1)*length(Cov2)*length(Cov3)
print(paste0("Total Permutations for Current Mediation Models: ", total_permutes))
# Step 2
# Create data.frame of output using model pre
## the var_permutations are run across the matrics into the function.
data_rep <-
apply(X = var_permutations, MARGIN = 1, function (combos) {
require(lavaan)
permuted.df <- df[combos] # pull data that only has variables we're interested in
# selection variable names to replace in model_fit()
X = as.character(combos[1])
Y = as.character(combos[2])
M = as.character(combos[3])
Age = as.character(combos[4])
Race = as.character(combos[5])
Sex = as.character(combos[6])
# we use "glue()" here in the model sytnax to example {variables} with the values assigned above.
mediation_model <- glue('
# Direct Effect (X->Y), c - path
{Y} ~ c*{X} + {Age} + {Race} + {Sex}
# Meidation (X -> M), a path
{M} ~ a*{X} + {Age} + {Race} + {Sex}
# Mediation (M -> Y), b path
{Y} ~ b*{M}
# Indirect Effect (a*b)
ind := a*b
# Total Effect
total := c + (a*b)
')
# Herre we run and save the model parameters to "fit"
fit <-lavaan::sem(model = mediation_model,
data = permuted.df,
se = "bootstrap",
bootstrap = model_boot,
mimic = "Mplus"
)
# Step 2
# Extra the variables we will be using in subsequent steps
require(parameters)
model_out = data.frame(model_parameters(fit, standardize = TRUE))
med_vars = lavNames(fit)
spec_data <- data.frame("X" = as.character(med_vars[3]),
"Y" = as.character(med_vars[1]),
"M" = as.character(med_vars[2]),
"Direct_estimate" = model_out[1,4], "Direct_std.error" = model_out[1,5],
"Direct_conf.low" = model_out[1,6], "Direct_conf.high" = model_out[1,7],
"Direct_p.value" = model_out[1,9],
"Apath_estimate" = model_out[5,4], "Apath_std.error" = model_out[5,5],
"Apath_conf.low" = model_out[5,6], "Apath_conf.high" = model_out[5,7],
"Apath_p.value" = model_out[5,9],
"Bpath_estimate" = model_out[9,4], "Bpath_std.error" = model_out[9,5],
"Bpath_conf.low" = model_out[9,6], "Bpath_conf.high" = model_out[9,7],
"Bpath_p.value" = model_out[9,9],
"Indirect_estimate" = model_out[16,4], "Indirect_std.error" = model_out[16,5],
"Indirect_conf.low" = model_out[16,6], "Indirect_conf.high" = model_out[16,7],
"Indirect_p.value" = model_out[16,9],
"Total_estimate" = model_out[17,4], "Total_std.error" = model_out[17,5],
"Total_conf.low" = model_out[17,6], "Total_conf.high" = model_out[17,7],
"Total_p.value" = model_out[17,9])
# add model observations & parameters N
spec_data$Observations = fitMeasures(fit)[21]; spec_data$N_Parameters = fitMeasures(fit)[1]
return(spec_data)
}) %>%
bind_rows() %>% # combined by rows the lists from lapply
gather(key = "Effect", value = "Coefficient", Direct_estimate:Total_p.value) %>% # we take all colum names Direct:Total_p and save the variable names, i.e., Direct, to the key "Effect" and the value to "Coefficient"
separate(col = "Effect", into = c("Effect","Type"), sep = "_", extra = "merge", fill = "right") %>% # because we want to combine this data to similar format as we safe our thijseen file, we want to separate all variables with '_', that way the first value 'Direct' or 'Apath' is in "Effect" and the type of value, i.e., SE or lower CI, is in 'Type' column. We fill these to the right
spread(key = "Type", value = "Coefficient") # now that we have the type column, we spread this data out from long to wide
}We extract the Betas and Standard errors from the original paper (i..e, Figure 1,
Table 4 - 6), and calculate the 95% CI for the Direct Effect, A path, B
path using Beta +/- 1.96 * SE. We save these in a
.csv file to use in comparisons here. Given that there was
not sufficient information to calculate these metrics based on
information provided in the original paper, Dr. Thijssen provided the
SE/95% CI values for total & indirect effects.
Of note: In the original pre-registration, we had a typo in the effect preregistered for the R/L Amyg CON indirect effect which didn’t reflect of those in Thijssen et al. 2021 reanalysis post philips scanner reported error by consortium, whereby we pre-registered an effect that was not the one the authors re-analyzed in their data after excluding later reported issue with philips scanner’s preprocessing. This did not change any subsequent inferences here.
Thij_eff <- read.csv("./Thijssenetal_Effects.csv") %>%
rename("estimate" = Beta, "std.error" = SE,
"conf.low" = lower_95CI, "conf.high" = upper_95CI,
"p.value" = pval)
# Remove the ACC_FA, as we are not using this in replication
Thij_eff <- Thij_eff %>%
filter(Brain != "ACC FA")Here we plot the Beta estimates and any associated 95% Confidence Interval for each path type: a path, b path, indirect (a*b), direct (c`) and total (c) across the five brain DVs.
color_1 <- cbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2")# six colours for six factors
Thij_eff %>%
ggplot(aes(x = Brain, y = estimate, colour = Brain)) +
#geom_errorbar(aes(ymin=len-ci, ymax=len+ci), colour="black", width=.1, position=pd) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, colour = Brain),
width = .1, alpha = .5) +
geom_point(fill = "white", shape = 21) +
labs(y = "Beta Estimates") +
scale_colour_manual(values = color_1)+
facet_wrap(~Effect, scale = "free")+
theme_minimal()+
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(),
axis.line.x = element_blank(), axis.text.x = element_blank())Thij_eff %>%
filter(Effect == "Direct" | Effect == "Indirect") %>%
ggplot(aes(x = Brain, y = estimate, colour = Brain)) +
#geom_errorbar(aes(ymin=len-ci, ymax=len+ci), colour="black", width=.1, position=pd) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, colour = Brain),
width = .1, alpha = .5) +
geom_point(fill = "white", shape = 21) +
labs(y = "Beta Estimates") +
scale_colour_manual(values = color_1)+
facet_wrap(~Effect, scale = "free")+
theme_minimal()+
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(),
axis.line.x = element_blank(), axis.text.x = element_blank())We use the same mediation model syntax as specified above.
Create combinations and run models combinations for IV, Mediation or Brain DVs to run 5 mediation. Here, we are running the variables for the replication:
IV: FamEnv_Fact, Meditor: p_puberty, DVs: Amygdala_vol, ACC_CT, ACC_CA, L_AmygCing_rest, R_AmygCing_rest
# list variables for mediation
Rep_iv = c("FamEnv_Fact")
Rep_dv = c("Amygdala_vol","ACC_CT","ACC_CA","L_AmygCing_rest","R_AmygCing_rest")
Rep_m = c("p_puberty")
# Future users, if permuting across covariates, uncomment below & update input/model code above
#Rep_cov = c("Age")Running the model. After the initial instance of running this model, we save the table output and do not run it next time. WIthout using the parallelized version of the multiverse function written above (which uses lapply), the duration of running this model on a macOS Monterey, 2.3GHz 8-code i9, 16GB ram is 36 minutes.
The code here below used to confirm initial instances of the mediation model, to ensure the models 1) run without major errors, 2) appropriate data is extracted and 3) no egregrious problems arise.
After the initial instance, the R code to run this model is not ran. Instead, after running the model the first time, we save the table with the estimates and load them back into Rstudio to use in subsequent steps. This is to save on machine resources/time and to increase efficiency in knitting the final version of the .html document.
Here we write the extract_mediate function. First, we
pull the associated X, Y, M variable names to replace the model values
with our variable names for the plots. Using the data pulled by
model_parameters, we pull the associated [row,column] from
the model_parameters() output for the model. Here we focus on the
Direct, Indirect, Total, Apath, and Bpath, extracting the beta, standard
error, association upper/lower bound 95% CI, and p-value
In the function we defined, `mediation_specr’, we model these parameters using lapply. This simply a function that can work across lists that we created – think of it as cleaner version of a for loop. We combined the extracted values across rows (bind_rows), then, we use a combination of gather, separate and spread to get ou data frame in the manner we want.
# load the prev. data
mediation_replicate <- read.csv("./Output/MedReplicate_Table_April_2022_renamed.csv")
# summarize the data
mediation_replicate %>%
kbl(digits = 4, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
full_width = F, html_font = "Times") | X | Y | M | Observations | N_Parameters | Effect | conf.high | conf.low | estimate | p.value | std.error |
|---|---|---|---|---|---|---|---|---|---|---|
| FamEnv Fact | ACC CA | PDS Par | 6658 | 13 | Apath | -0.1420 | -0.1835 | -0.1627 | 0.0000 | 0.0106 |
| FamEnv Fact | ACC CA | PDS Par | 6658 | 13 | Bpath | 0.0659 | 0.0078 | 0.0369 | 0.0128 | 0.0148 |
| FamEnv Fact | ACC CA | PDS Par | 6658 | 13 | Direct | 0.0208 | -0.0287 | -0.0040 | 0.7535 | 0.0126 |
| FamEnv Fact | ACC CA | PDS Par | 6658 | 13 | Indirect | -0.0012 | -0.0108 | -0.0060 | 0.0140 | 0.0024 |
| FamEnv Fact | ACC CA | PDS Par | 6658 | 13 | Total | 0.0144 | -0.0343 | -0.0100 | 0.4222 | 0.0124 |
| FamEnv Fact | ACC CT | PDS Par | 6658 | 13 | Apath | -0.1420 | -0.1832 | -0.1626 | 0.0000 | 0.0105 |
| FamEnv Fact | ACC CT | PDS Par | 6658 | 13 | Bpath | -0.0188 | -0.0787 | -0.0487 | 0.0014 | 0.0153 |
| FamEnv Fact | ACC CT | PDS Par | 6658 | 13 | Direct | 0.0066 | -0.0449 | -0.0191 | 0.1458 | 0.0132 |
| FamEnv Fact | ACC CT | PDS Par | 6658 | 13 | Indirect | 0.0129 | 0.0030 | 0.0079 | 0.0016 | 0.0025 |
| FamEnv Fact | ACC CT | PDS Par | 6658 | 13 | Total | 0.0140 | -0.0364 | -0.0112 | 0.3837 | 0.0129 |
| FamEnv Fact | Amygdala vol | PDS Par | 6658 | 13 | Apath | -0.1422 | -0.1833 | -0.1627 | 0.0000 | 0.0105 |
| FamEnv Fact | Amygdala vol | PDS Par | 6658 | 13 | Bpath | 0.0544 | -0.0036 | 0.0254 | 0.0857 | 0.0148 |
| FamEnv Fact | Amygdala vol | PDS Par | 6658 | 13 | Direct | 0.0239 | -0.0255 | -0.0008 | 0.9497 | 0.0126 |
| FamEnv Fact | Amygdala vol | PDS Par | 6658 | 13 | Indirect | 0.0006 | -0.0089 | -0.0041 | 0.0872 | 0.0024 |
| FamEnv Fact | Amygdala vol | PDS Par | 6658 | 13 | Total | 0.0193 | -0.0292 | -0.0049 | 0.6898 | 0.0124 |
| FamEnv Fact | L AmygCON | PDS Par | 6658 | 13 | Apath | -0.1421 | -0.1839 | -0.1630 | 0.0000 | 0.0106 |
| FamEnv Fact | L AmygCON | PDS Par | 6658 | 13 | Bpath | -0.0414 | -0.1018 | -0.0716 | 0.0000 | 0.0154 |
| FamEnv Fact | L AmygCON | PDS Par | 6658 | 13 | Direct | 0.1210 | 0.0731 | 0.0971 | 0.0000 | 0.0122 |
| FamEnv Fact | L AmygCON | PDS Par | 6658 | 13 | Indirect | 0.0168 | 0.0065 | 0.0117 | 0.0000 | 0.0026 |
| FamEnv Fact | L AmygCON | PDS Par | 6658 | 13 | Total | 0.1323 | 0.0852 | 0.1088 | 0.0000 | 0.0120 |
| FamEnv Fact | R AmygCON | PDS Par | 6658 | 13 | Apath | -0.1422 | -0.1834 | -0.1628 | 0.0000 | 0.0105 |
| FamEnv Fact | R AmygCON | PDS Par | 6658 | 13 | Bpath | -0.0356 | -0.0942 | -0.0649 | 0.0000 | 0.0149 |
| FamEnv Fact | R AmygCON | PDS Par | 6658 | 13 | Direct | 0.0995 | 0.0502 | 0.0748 | 0.0000 | 0.0126 |
| FamEnv Fact | R AmygCON | PDS Par | 6658 | 13 | Indirect | 0.0156 | 0.0056 | 0.0106 | 0.0000 | 0.0025 |
| FamEnv Fact | R AmygCON | PDS Par | 6658 | 13 | Total | 0.1097 | 0.0611 | 0.0854 | 0.0000 | 0.0124 |
First, we pull Brain variable and indirect/direct/total/apath/bpath betas and associated p.values from our replication model for the 5 DVs
replication_to_compare <- mediation_replicate %>%
dplyr::select(Y, Effect, estimate, p.value, conf.high, conf.low) %>%
rename("Brain" = Y) For the conceptual replication, we proposed to check several things:
First, we provide a visual representation of item 1 - 3 noted above.
We overlay the original study beta estimates + 95% CI and the
replication model beta estimates for each mediation path across
[5] brain DVs mediation models. The original beta estimate
is a colored circle (differentiated by path) and the replicated estimate
is a colored ‘X’ associated with a similar color for each mediation path
and brain DV.
We focus on the 95% CI due to the fact that across all random samples for the size of this data, 95% of those samples will have a 95% confidence interval that will contain the estimated value that represents the data with similar characteristics. In other words, if we sampled 100 participants, got our means and SD and we were to repeatedly sample based on the sample size, mean, and SD we would get an estimate with a lower and upper bound, or our 95% CI.
color_1 <- cbPalette <- c("black", "chocolate1", "firebrick", "slateblue", "turquoise4")# six colours for six factors
#jpeg("Figures/Aim1/ReplicBeta_v_Orig95ci.jpeg", units = "in",
# width = 8, height = 5, res = 300)
Thij_eff %>%
#filter(!Effect == "Total") %>%
ggplot(aes(x = Brain, y = estimate, colour = Brain)) +
#geom_errorbar(aes(ymin=len-ci, ymax=len+ci), colour="black", width=.1, position=pd) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, colour = Brain),
width = .1, alpha = .5) +
geom_point(fill = "white", shape = 21) +
geom_point(data = replication_to_compare, aes(x = Brain, y = estimate, colour = Brain),
shape = 4, position = "jitter") +
labs(y = "Beta Estimates",
colour = "Brain Outcome"
#caption = "o = β From Thijssen et al. \n x: β From Replication"
) +
scale_colour_manual(values = color_1)+
facet_wrap(~Effect, scale = "free")+
theme_minimal()+
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(),
axis.line.x = element_blank(), axis.text.x = element_blank())#dev.off()
sub_effects_a <- Thij_eff %>%
filter(Effect == "Direct" | Effect == "Indirect") %>%
ggplot(aes(x = Brain, y = estimate, colour = Brain)) +
#geom_errorbar(aes(ymin=len-ci, ymax=len+ci), colour="black", width=.1, position=pd) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, colour = Brain),
width = .1, alpha = .5) +
geom_point(fill = "white", shape = 21) +
geom_point(data = replication_to_compare %>% filter(Effect == "Direct" | Effect == "Indirect"), aes(x = Brain, y = estimate, colour = Brain),
shape = 4, position = "jitter") +
labs(y = "Beta Estimates",
colour = "Brain Outcome"
#caption = "o = β From Thijssen et al. \n x: β From Replication"
) +
scale_colour_manual(values = color_1)+
facet_wrap(~Effect, scale = "free")+
theme_minimal()+
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(),
axis.line.x = element_blank(), axis.text.x = element_blank(),
legend.position = "none")Here, we plot the inverse of the above. Above we
visualized the beta estimates &
95% CI from the original study and how the replicated beta
estimates overlapped with this. Here, we visualize the
replicated beta estimates & their associated 95% and
see how the original beta estimates replicate with them.
color_1 <- cbPalette <- c("black", "chocolate1", "firebrick", "slateblue", "turquoise4")# six colours for six factors
#jpeg("Figures/Aim1/OrigBeta_v_Repl95ci.jpeg", units = "in",
# width = 8, height = 5, res = 300)
replication_to_compare %>%
#filter(!Effect == "Total") %>%
ggplot(aes(x = Brain, y = estimate, colour = Brain)) +
#geom_errorbar(aes(ymin=len-ci, ymax=len+ci), colour="black", width=.1, position=pd) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, colour = Brain),
width = .1, alpha = .5) +
geom_point(fill = "white", shape = 4) +
geom_point(data = Thij_eff, aes(x = Brain, y = estimate, colour = Brain),
shape = 21, position = "jitter") +
labs(y = "Beta Estimates",
colour = "Brain Outcome"
# caption = "o = β From Thijssen et al. \n x: β From Replication"
) +
scale_colour_manual(values = color_1)+
facet_wrap(~Effect, scale = "free")+
theme_minimal()+
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(),
axis.line.x = element_blank(), axis.text.x = element_blank())#dev.off()
sub_effects_b <- replication_to_compare %>%
filter(Effect == "Direct" | Effect == "Indirect") %>%
ggplot(aes(x = Brain, y = estimate, colour = Brain)) +
#geom_errorbar(aes(ymin=len-ci, ymax=len+ci), colour="black", width=.1, position=pd) +
geom_errorbar(aes(ymin = conf.low, ymax = conf.high, colour = Brain),
width = .1, alpha = .5) +
geom_point(fill = "white", shape = 4) +
geom_point(data = Thij_eff %>% filter(Effect == "Direct" | Effect == "Indirect"),
aes(x = Brain, y = estimate, colour = Brain),
shape = 21, position = "jitter") +
labs(y = "",
colour = "Brain Outcome") +
scale_colour_manual(values = color_1)+
facet_wrap(~Effect, scale = "free")+
theme_minimal()+
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(),
axis.line.x = element_blank(), axis.text.x = element_blank())
#jpeg("Figures/Aim1/Combined_DirInd_plt.jpeg", units = "in",
# width = 8, height = 5, res = 300)
#sub_effects_a / sub_effects_b + plot_layout(guides = 'collect')
cowplot::plot_grid(sub_effects_a, sub_effects_b, ncol = 1, align = "v", axis = 'tblr',
labels = c('A', 'B'), rel_heights = c(1, 1),
label_fontfamily = "Times", label_size = 14)#dev.off()To understand the confidence intervals a bit better, here we plot the
distributions of the 5 brain outcomes. This may help inform why the
confidence intervals are wider for some brain, such as ACC CA/CT and
volumen, but not others, such as the L/R amygdala & CING
connectivity. We plot this in two different ways, where the y-scale is
fixed across all of the variables and where the y-scale is
free to vary across the brain types.
data %>%
pivot_longer(c(Amygdala_vol,ACC_CT,ACC_CA,
L_AmygCing_rest,R_AmygCing_rest),
names_to = "Brain_ROI", values_to = "Values") %>%
ggplot(aes(x = Values)) +
geom_density() +
facet_wrap(vars(Brain_ROI), ncol = 3) +
labs(title = "Fixed scale", x = "Brain ROI", y = "Value Distributions",
caption = "vol = volume; ACC = Anterior Cingulate Cortex; CT = cortical thickness; /n
CA = cortical area; L = Left, R = Right; AmygCing_rest = Amygdala-CON rsfMRI") +
theme_minimal()data %>%
pivot_longer(c(Amygdala_vol,ACC_CT,ACC_CA,
L_AmygCing_rest,R_AmygCing_rest),
names_to = "Brain_ROI", values_to = "Values") %>%
ggplot(aes(x = Values)) +
geom_density() +
facet_wrap(vars(Brain_ROI), ncol = 3, scales = "free") +
labs(title = "Free scale", x = "Brain ROI", y = "Value Distributions",
caption = "vol = volume; ACC = Anterior Cingulate Cortex; CT = cortical thickness; /n
CA = cortical area; L = Left, R = Right; AmygCing_rest = Amygdala-CON rsfMRI") +
theme_minimal()Here, we generate some estimates to determine p-value (i.e., p < .001, < .01, < .05) threshold for each estimate using case_when. Then, we ask a True (1)/False(0) statement using if_else within mutate to determine whether 1) the sign of the effect (+/-) is in the same direction using the sign function, 2) the effect from replicated data found between 95% CI in original study and 2) that the original p-value category (i.e., p > 05 or p < .05) is similar across the two. Before we do this, we combine the original data output (dataframe(Thij_eff) and the replicated data (data.frame(replication_to_compare)) by the “brain” and the “effect” overlap. We provide the suffix “_OG” for the original coefficients and “_Rep” for the replication.
combined_OG_Rep <-left_join(Thij_eff, replication_to_compare,
by = c("Brain","Effect"),
suffix = c("_OG","_Rep"))
combined_OG_Rep <- combined_OG_Rep %>%
mutate(pval_Rep = case_when(p.value_Rep < .05 ~ "< .05",
TRUE ~ "> .05")) %>%
mutate(RepB_BtwnOrig_95CI =
if_else(estimate_Rep < conf.high_OG & conf.low_OG < estimate_Rep,1,0),
OrigB_BtwnRep_95CI =
if_else(estimate_OG < conf.high_Rep & conf.low_Rep < estimate_OG,1,0),
same_p_cat = if_else(p.value_OG == pval_Rep,1,0),
same_sign = if_else(sign(estimate_Rep) == sign(estimate_OG), 1, 0)
)
write.csv(x = combined_OG_Rep, file = "./Output/Effect_OGRep_Overlap.csv", row.names = F)Now that we have generated our variables, we can quantify the % of which overlap by using the mean() within the summarize() wrap. Given that our values are 1/0, using the mean works, given that we add/divide.
For all parameter estimates (total, direct, indirect, a/b path)
combined_OG_Rep %>%
dplyr::summarize('RepBeta_btwn_95CI_OfOrig' = mean(RepB_BtwnOrig_95CI, na.rm = T),
'OrigBeta_btwn_95CI_OfRep' = mean(OrigB_BtwnRep_95CI, na.rm = T),
'RepP_SameCategory_AsOrig' = mean(same_p_cat, na.rm = T),
'RepBetaSign_SameDir_AsOrig' = mean(same_sign, na.rm = T),)## RepBeta_btwn_95CI_OfOrig OrigBeta_btwn_95CI_OfRep RepP_SameCategory_AsOrig
## 1 0.92 0.48 0.68
## RepBetaSign_SameDir_AsOrig
## 1 0.84
#jpeg("Figures/Aim1/RepOrigOverlap_Barplot.jpeg", units = "in",
# width = 10, height = 5, res = 300)
combined_OG_Rep %>%
dplyr::summarize('RepBeta_btwn_95CI_OfOrig' = mean(RepB_BtwnOrig_95CI, na.rm = T),
'OrigBeta_btwn_95CI_OfRep' = mean(OrigB_BtwnRep_95CI, na.rm = T),
'RepP_SameCategory_AsOrig' = mean(same_p_cat, na.rm = T),
'RepBetaSign_SameDir_AsOrig' = mean(same_sign, na.rm = T)) %>%
gather(., key = X, value = Y, RepBeta_btwn_95CI_OfOrig,OrigBeta_btwn_95CI_OfRep,
RepP_SameCategory_AsOrig, RepBetaSign_SameDir_AsOrig) %>%
ggplot(aes(x = X, y = Y, fill = X)) +
geom_bar(stat = "identity") +
labs(col = "Replicated Estimates")+
ylim(c(0.0,1.0))+
ylab("Percentage") +
xlab("")+
theme_minimal() +
theme(axis.text.x = element_blank(), legend.title = element_blank(),
legend.position = "bottom")+
scale_fill_manual(values=c("#999999", "coral2", "#E69F00", "#56B4E9"),
labels = c(
"Original β Between \n95% CI for Replication β",
"Replication β Between \n95% CI around Original β",
"Replication β Sign Same \nDirection As Original β",
"Replication p-val Same \nCategory as Original p-val"))#dev.off()For Indirect and Direct paths only, as preregistered for Aim 1
combined_OG_Rep %>%
filter(Effect == "Direct" | Effect == "Indirect") %>%
dplyr::summarize('RepBeta_btwn_95CI_OfOrig' = mean(RepB_BtwnOrig_95CI, na.rm = T),
'OrigBeta_btwn_95CI_OfRep' = mean(OrigB_BtwnRep_95CI, na.rm = T),
'RepP_SameCategory_AsOrig' = mean(same_p_cat, na.rm = T),
'RepBetaSign_SameDir_AsOrig' = mean(same_sign, na.rm = T),)## RepBeta_btwn_95CI_OfOrig OrigBeta_btwn_95CI_OfRep RepP_SameCategory_AsOrig
## 1 1 0.5 0.6
## RepBetaSign_SameDir_AsOrig
## 1 0.9
#jpeg("Figures/Aim1/RepOrigOverlap_Barplot_DirIndOnly.jpeg", units = "in",
# width = 10, height = 5, res = 300)
combined_OG_Rep %>%
filter(Effect == "Direct" | Effect == "Indirect") %>%
dplyr::summarize('RepBeta_btwn_95CI_OfOrig' = mean(RepB_BtwnOrig_95CI, na.rm = T),
'OrigBeta_btwn_95CI_OfRep' = mean(OrigB_BtwnRep_95CI, na.rm = T),
'RepP_SameCategory_AsOrig' = mean(same_p_cat, na.rm = T),
'RepBetaSign_SameDir_AsOrig' = mean(same_sign, na.rm = T)) %>%
gather(., key = X, value = Y, RepBeta_btwn_95CI_OfOrig,OrigBeta_btwn_95CI_OfRep,
RepP_SameCategory_AsOrig, RepBetaSign_SameDir_AsOrig) %>%
ggplot(aes(x = X, y = Y, fill = X)) +
geom_bar(stat = "identity") +
labs(col = "Replicated Estimates")+
ylim(c(0.0,1.0))+
ylab("Percentage") +
xlab("")+
theme_minimal() +
theme(axis.text.x = element_blank(), legend.title = element_blank(),
legend.position = "bottom")+
scale_fill_manual(values=c("#999999", "coral2", "#E69F00", "#56B4E9"),
labels = c(
"Original β Between \n95% CI for Replication β",
"Replication β Between \n95% CI around Original β",
"Replication β Sign Same \nDirection As Original β",
"Replication p-val Same \nCategory as Original p-val"))#dev.off()Here we randomly select 5 co-authors out of a list of 8 to subjectively evaluate the replication of the effects. As preregistered, we do not include original authors in this list.
First, we randomly generate a seed between 1 and 1000, and then randomly sample 5 names from a list of 8, without replacement. We randomize the seed so the resulting values are truly random. Otherwise, by having a list of 8 values we would know prospectively which 8 would be selected based for a set.seed of say, 100.
Note, after the initial run, to avoid bias this code is not rerun.
Thus, a single file is generated. The initial code was ran one day after
the preregistration: February 19th, 2022, 9:45AM PST.
subj_tally <- read.csv("./Output/Replication_Tally_plt.csv")
colors <- cbPalette <- c("black", "chocolate1", "firebrick", "slateblue", "turquoise4")
#jpeg("Figures/Aim1/Replication_Agreement.jpeg", units = "in",
# width = 10, height = 5, res = 300)
subj_tally %>%
ggplot(aes(x = Brain, y = Overall_Percent, fill = Brain)) +
geom_bar(stat = "identity") +
labs(col = "Replicated Estimates", caption = "Dotted, Dashed line = 50% Agreement")+
geom_hline(yintercept = .50, linetype = "dotdash", colour = "grey")+
ylim(c(0.0,1.0))+
ylab("Percentage Agreement (%)") +
xlab("")+
facet_wrap(~Effect)+
scale_fill_manual(values = colors)+
theme_minimal() +
theme(axis.text.x = element_blank(), legend.title = element_blank(),
legend.position = "bottom")#dev.off()Here we create a list of variables that we will be using. For example, we are running models across different combinations of IV, DV, and Mediators. We add these to a list.
In our function, we use exapnd.grid to create all
permutations for our “X”, “Y” and “M” columns that’ll be used in the
mediation model. We save this as as a matrix.
Recall, we have reverse coded variables in prior R steps (such as FES youth, FES Parent, Par Factor in Part2 & 3) to maintain consistency in the interpretation. In otherwords, higher scores on the variable reflect more positive (i.e., less stressful) environment.
# list variables for mediation
IVs = c("FamEnv_Fact","Demo_Fact","Child_Fact","Par_Fact",
"FES_youth","FES_parent",
"PMON","CRPBI","Avg_IncomeEduc")
M = c("p_puberty","y_puberty","avg_puberty")
DVs = c("Amygdala_vol","ACC_CT","ACC_CA","L_AmygCing_rest","R_AmygCing_rest")In the real models we will fit a mediation model degenerated from Steps 1 - 3. This, of course, would be expanded by including the brain variables. The goal would be to create a loop/function that will run the mediation with each version of:
Family
Puberty
Brain
While for the real analyses we will use the variables from the original data, here we will focus on our simulated variables. So the mediation model will be comprised using these fake variables.
Here we take the permutations of our X, Y, M variables, combined it
with apply() to run a quick function that will run our mediation models
using lavaan. For specifics related to this function, revisit
Multiverse Function above
Running the multivers model. After the initial instance of running this model, we save the table output and do not run it subsequent times R is restarted. Without using the parallelized version of the multiverse function written above (which uses lapply), the duration of running this model on a macOS Monterey, 2.3GHz 8-code i9, 16GB ram is ~ 16hrs.
here we extract and review the variables in the model + standardized parameters, standard error, 95% CI (lb/ub) and p-value.
Below we can visualize this complete set out coefficients for our models. This output is associated with the length N = 135 models that we were intending to get. This is product for X, M, Y.
# Load in multiverse tables
mediation_multiverse <- read.csv("./Output/Multiverse_Table_July_2022_renamed.csv")
mediation_multiverse %>%
kbl(digits = 4, booktabs = TRUE) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
full_width = F, html_font = "Times") | X | Y | M | Observations | N_Parameters | Effect | conf.high | conf.low | estimate | p.value | std.error |
|---|---|---|---|---|---|---|---|---|---|---|
| Avg IncEdu | ACC CA | PDS ParYth | 6063 | 13 | Apath | -0.1647 | -0.2170 | -0.1909 | 0.0000 | 0.0133 |
| Avg IncEdu | ACC CA | PDS ParYth | 6063 | 13 | Bpath | 0.0445 | -0.0188 | 0.0129 | 0.4257 | 0.0161 |
| Avg IncEdu | ACC CA | PDS ParYth | 6063 | 13 | Direct | -0.0158 | -0.0688 | -0.0423 | 0.0018 | 0.0135 |
| Avg IncEdu | ACC CA | PDS ParYth | 6063 | 13 | Indirect | 0.0036 | -0.0085 | -0.0025 | 0.4268 | 0.0031 |
| Avg IncEdu | ACC CA | PDS ParYth | 6063 | 13 | Total | -0.0188 | -0.0706 | -0.0447 | 0.0007 | 0.0132 |
| Avg IncEdu | ACC CA | PDS Par | 6063 | 13 | Apath | -0.1561 | -0.2041 | -0.1801 | 0.0000 | 0.0122 |
| Avg IncEdu | ACC CA | PDS Par | 6063 | 13 | Bpath | 0.0650 | 0.0042 | 0.0346 | 0.0259 | 0.0155 |
| Avg IncEdu | ACC CA | PDS Par | 6063 | 13 | Direct | -0.0114 | -0.0656 | -0.0385 | 0.0054 | 0.0138 |
| Avg IncEdu | ACC CA | PDS Par | 6063 | 13 | Indirect | -0.0007 | -0.0118 | -0.0062 | 0.0278 | 0.0028 |
| Avg IncEdu | ACC CA | PDS Par | 6063 | 13 | Total | -0.0182 | -0.0712 | -0.0447 | 0.0010 | 0.0135 |
| Avg IncEdu | ACC CA | PDS Yth | 6063 | 13 | Apath | -0.1179 | -0.1749 | -0.1464 | 0.0000 | 0.0145 |
| Avg IncEdu | ACC CA | PDS Yth | 6063 | 13 | Bpath | 0.0271 | -0.0304 | -0.0016 | 0.9121 | 0.0147 |
| Avg IncEdu | ACC CA | PDS Yth | 6063 | 13 | Direct | -0.0181 | -0.0719 | -0.0450 | 0.0011 | 0.0137 |
| Avg IncEdu | ACC CA | PDS Yth | 6063 | 13 | Indirect | 0.0044 | -0.0040 | 0.0002 | 0.9121 | 0.0021 |
| Avg IncEdu | ACC CA | PDS Yth | 6063 | 13 | Total | -0.0182 | -0.0713 | -0.0447 | 0.0010 | 0.0135 |
| Avg IncEdu | ACC CT | PDS ParYth | 6063 | 13 | Apath | -0.1645 | -0.2172 | -0.1909 | 0.0000 | 0.0134 |
| Avg IncEdu | ACC CT | PDS ParYth | 6063 | 13 | Bpath | 0.0032 | -0.0617 | -0.0292 | 0.0775 | 0.0166 |
| Avg IncEdu | ACC CT | PDS ParYth | 6063 | 13 | Direct | 0.0425 | -0.0120 | 0.0152 | 0.2727 | 0.0139 |
| Avg IncEdu | ACC CT | PDS ParYth | 6063 | 13 | Indirect | 0.0118 | -0.0007 | 0.0056 | 0.0803 | 0.0032 |
| Avg IncEdu | ACC CT | PDS ParYth | 6063 | 13 | Total | 0.0473 | -0.0056 | 0.0208 | 0.1226 | 0.0135 |
| Avg IncEdu | ACC CT | PDS Par | 6063 | 13 | Apath | -0.1567 | -0.2033 | -0.1800 | 0.0000 | 0.0119 |
| Avg IncEdu | ACC CT | PDS Par | 6063 | 13 | Bpath | -0.0162 | -0.0789 | -0.0476 | 0.0030 | 0.0160 |
| Avg IncEdu | ACC CT | PDS Par | 6063 | 13 | Direct | 0.0391 | -0.0146 | 0.0122 | 0.3728 | 0.0137 |
| Avg IncEdu | ACC CT | PDS Par | 6063 | 13 | Indirect | 0.0143 | 0.0028 | 0.0086 | 0.0035 | 0.0029 |
| Avg IncEdu | ACC CT | PDS Par | 6063 | 13 | Total | 0.0469 | -0.0053 | 0.0208 | 0.1186 | 0.0133 |
| Avg IncEdu | ACC CT | PDS Yth | 6063 | 13 | Apath | -0.1177 | -0.1752 | -0.1464 | 0.0000 | 0.0147 |
| Avg IncEdu | ACC CT | PDS Yth | 6063 | 13 | Bpath | 0.0232 | -0.0343 | -0.0055 | 0.7057 | 0.0147 |
| Avg IncEdu | ACC CT | PDS Yth | 6063 | 13 | Direct | 0.0474 | -0.0074 | 0.0200 | 0.1525 | 0.0140 |
| Avg IncEdu | ACC CT | PDS Yth | 6063 | 13 | Indirect | 0.0050 | -0.0034 | 0.0008 | 0.7060 | 0.0022 |
| Avg IncEdu | ACC CT | PDS Yth | 6063 | 13 | Total | 0.0478 | -0.0061 | 0.0208 | 0.1302 | 0.0138 |
| Avg IncEdu | Amygdala vol | PDS ParYth | 6063 | 13 | Apath | -0.1645 | -0.2173 | -0.1909 | 0.0000 | 0.0135 |
| Avg IncEdu | Amygdala vol | PDS ParYth | 6063 | 13 | Bpath | 0.0598 | -0.0043 | 0.0277 | 0.0895 | 0.0163 |
| Avg IncEdu | Amygdala vol | PDS ParYth | 6063 | 13 | Direct | 0.0321 | -0.0209 | 0.0056 | 0.6784 | 0.0135 |
| Avg IncEdu | Amygdala vol | PDS ParYth | 6063 | 13 | Indirect | 0.0009 | -0.0114 | -0.0053 | 0.0916 | 0.0031 |
| Avg IncEdu | Amygdala vol | PDS ParYth | 6063 | 13 | Total | 0.0261 | -0.0255 | 0.0003 | 0.9816 | 0.0132 |
| Avg IncEdu | Amygdala vol | PDS Par | 6063 | 13 | Apath | -0.1560 | -0.2042 | -0.1801 | 0.0000 | 0.0123 |
| Avg IncEdu | Amygdala vol | PDS Par | 6063 | 13 | Bpath | 0.0563 | -0.0039 | 0.0262 | 0.0875 | 0.0153 |
| Avg IncEdu | Amygdala vol | PDS Par | 6063 | 13 | Direct | 0.0308 | -0.0207 | 0.0051 | 0.7010 | 0.0132 |
| Avg IncEdu | Amygdala vol | PDS Par | 6063 | 13 | Indirect | 0.0007 | -0.0102 | -0.0047 | 0.0893 | 0.0028 |
| Avg IncEdu | Amygdala vol | PDS Par | 6063 | 13 | Total | 0.0257 | -0.0250 | 0.0003 | 0.9796 | 0.0129 |
| Avg IncEdu | Amygdala vol | PDS Yth | 6063 | 13 | Apath | -0.1176 | -0.1754 | -0.1465 | 0.0000 | 0.0148 |
| Avg IncEdu | Amygdala vol | PDS Yth | 6063 | 13 | Bpath | 0.0417 | -0.0159 | 0.0129 | 0.3793 | 0.0147 |
| Avg IncEdu | Amygdala vol | PDS Yth | 6063 | 13 | Direct | 0.0278 | -0.0234 | 0.0022 | 0.8671 | 0.0131 |
| Avg IncEdu | Amygdala vol | PDS Yth | 6063 | 13 | Indirect | 0.0023 | -0.0061 | -0.0019 | 0.3805 | 0.0022 |
| Avg IncEdu | Amygdala vol | PDS Yth | 6063 | 13 | Total | 0.0256 | -0.0250 | 0.0003 | 0.9819 | 0.0129 |
| Avg IncEdu | L AmygCON | PDS ParYth | 6063 | 13 | Apath | -0.1646 | -0.2175 | -0.1911 | 0.0000 | 0.0135 |
| Avg IncEdu | L AmygCON | PDS ParYth | 6063 | 13 | Bpath | -0.0147 | -0.0825 | -0.0486 | 0.0050 | 0.0173 |
| Avg IncEdu | L AmygCON | PDS ParYth | 6063 | 13 | Direct | 0.1544 | 0.0968 | 0.1256 | 0.0000 | 0.0147 |
| Avg IncEdu | L AmygCON | PDS ParYth | 6063 | 13 | Indirect | 0.0159 | 0.0026 | 0.0093 | 0.0061 | 0.0034 |
| Avg IncEdu | L AmygCON | PDS ParYth | 6063 | 13 | Total | 0.1627 | 0.1070 | 0.1349 | 0.0000 | 0.0142 |
| Avg IncEdu | L AmygCON | PDS Par | 6063 | 13 | Apath | -0.1566 | -0.2040 | -0.1803 | 0.0000 | 0.0121 |
| Avg IncEdu | L AmygCON | PDS Par | 6063 | 13 | Bpath | -0.0404 | -0.1027 | -0.0716 | 0.0000 | 0.0159 |
| Avg IncEdu | L AmygCON | PDS Par | 6063 | 13 | Direct | 0.1502 | 0.0941 | 0.1222 | 0.0000 | 0.0143 |
| Avg IncEdu | L AmygCON | PDS Par | 6063 | 13 | Indirect | 0.0188 | 0.0070 | 0.0129 | 0.0000 | 0.0030 |
| Avg IncEdu | L AmygCON | PDS Par | 6063 | 13 | Total | 0.1625 | 0.1077 | 0.1351 | 0.0000 | 0.0140 |
| Avg IncEdu | L AmygCON | PDS Yth | 6063 | 13 | Apath | -0.1181 | -0.1748 | -0.1465 | 0.0000 | 0.0145 |
| Avg IncEdu | L AmygCON | PDS Yth | 6063 | 13 | Bpath | 0.0162 | -0.0439 | -0.0139 | 0.3651 | 0.0153 |
| Avg IncEdu | L AmygCON | PDS Yth | 6063 | 13 | Direct | 0.1612 | 0.1045 | 0.1329 | 0.0000 | 0.0145 |
| Avg IncEdu | L AmygCON | PDS Yth | 6063 | 13 | Indirect | 0.0065 | -0.0024 | 0.0020 | 0.3676 | 0.0023 |
| Avg IncEdu | L AmygCON | PDS Yth | 6063 | 13 | Total | 0.1627 | 0.1071 | 0.1349 | 0.0000 | 0.0142 |
| Avg IncEdu | R AmygCON | PDS ParYth | 6063 | 13 | Apath | -0.1650 | -0.2166 | -0.1908 | 0.0000 | 0.0132 |
| Avg IncEdu | R AmygCON | PDS ParYth | 6063 | 13 | Bpath | -0.0077 | -0.0729 | -0.0403 | 0.0154 | 0.0166 |
| Avg IncEdu | R AmygCON | PDS ParYth | 6063 | 13 | Direct | 0.1353 | 0.0791 | 0.1072 | 0.0000 | 0.0143 |
| Avg IncEdu | R AmygCON | PDS ParYth | 6063 | 13 | Indirect | 0.0140 | 0.0013 | 0.0077 | 0.0175 | 0.0032 |
| Avg IncEdu | R AmygCON | PDS ParYth | 6063 | 13 | Total | 0.1422 | 0.0876 | 0.1149 | 0.0000 | 0.0139 |
| Avg IncEdu | R AmygCON | PDS Par | 6063 | 13 | Apath | -0.1568 | -0.2035 | -0.1801 | 0.0000 | 0.0119 |
| Avg IncEdu | R AmygCON | PDS Par | 6063 | 13 | Bpath | -0.0378 | -0.0967 | -0.0672 | 0.0000 | 0.0150 |
| Avg IncEdu | R AmygCON | PDS Par | 6063 | 13 | Direct | 0.1308 | 0.0751 | 0.1030 | 0.0000 | 0.0142 |
| Avg IncEdu | R AmygCON | PDS Par | 6063 | 13 | Indirect | 0.0177 | 0.0066 | 0.0121 | 0.0000 | 0.0028 |
| Avg IncEdu | R AmygCON | PDS Par | 6063 | 13 | Total | 0.1426 | 0.0876 | 0.1151 | 0.0000 | 0.0140 |
| Avg IncEdu | R AmygCON | PDS Yth | 6063 | 13 | Apath | -0.1179 | -0.1749 | -0.1464 | 0.0000 | 0.0145 |
| Avg IncEdu | R AmygCON | PDS Yth | 6063 | 13 | Bpath | 0.0225 | -0.0380 | -0.0077 | 0.6173 | 0.0154 |
| Avg IncEdu | R AmygCON | PDS Yth | 6063 | 13 | Direct | 0.1420 | 0.0856 | 0.1138 | 0.0000 | 0.0144 |
| Avg IncEdu | R AmygCON | PDS Yth | 6063 | 13 | Indirect | 0.0056 | -0.0033 | 0.0011 | 0.6186 | 0.0023 |
| Avg IncEdu | R AmygCON | PDS Yth | 6063 | 13 | Total | 0.1426 | 0.0873 | 0.1149 | 0.0000 | 0.0141 |
| Child Fact | ACC CA | PDS ParYth | 6646 | 13 | Apath | -0.0731 | -0.1227 | -0.0979 | 0.0000 | 0.0126 |
| Child Fact | ACC CA | PDS ParYth | 6646 | 13 | Bpath | 0.0485 | -0.0131 | 0.0177 | 0.2606 | 0.0157 |
| Child Fact | ACC CA | PDS ParYth | 6646 | 13 | Direct | 0.0613 | 0.0136 | 0.0375 | 0.0021 | 0.0122 |
| Child Fact | ACC CA | PDS ParYth | 6646 | 13 | Indirect | 0.0013 | -0.0048 | -0.0017 | 0.2644 | 0.0016 |
| Child Fact | ACC CA | PDS ParYth | 6646 | 13 | Total | 0.0594 | 0.0121 | 0.0358 | 0.0030 | 0.0121 |
| Child Fact | ACC CA | PDS Par | 6646 | 13 | Apath | -0.0267 | -0.0694 | -0.0480 | 0.0000 | 0.0109 |
| Child Fact | ACC CA | PDS Par | 6646 | 13 | Bpath | 0.0690 | 0.0123 | 0.0407 | 0.0049 | 0.0145 |
| Child Fact | ACC CA | PDS Par | 6646 | 13 | Direct | 0.0613 | 0.0141 | 0.0377 | 0.0018 | 0.0121 |
| Child Fact | ACC CA | PDS Par | 6646 | 13 | Indirect | -0.0003 | -0.0036 | -0.0020 | 0.0170 | 0.0008 |
| Child Fact | ACC CA | PDS Par | 6646 | 13 | Total | 0.0594 | 0.0121 | 0.0357 | 0.0030 | 0.0121 |
| Child Fact | ACC CA | PDS Yth | 6646 | 13 | Apath | -0.0814 | -0.1349 | -0.1081 | 0.0000 | 0.0137 |
| Child Fact | ACC CA | PDS Yth | 6646 | 13 | Bpath | 0.0295 | -0.0249 | 0.0023 | 0.8690 | 0.0139 |
| Child Fact | ACC CA | PDS Yth | 6646 | 13 | Direct | 0.0598 | 0.0122 | 0.0360 | 0.0030 | 0.0121 |
| Child Fact | ACC CA | PDS Yth | 6646 | 13 | Indirect | 0.0027 | -0.0032 | -0.0002 | 0.8691 | 0.0015 |
| Child Fact | ACC CA | PDS Yth | 6646 | 13 | Total | 0.0594 | 0.0121 | 0.0357 | 0.0031 | 0.0121 |
| Child Fact | ACC CT | PDS ParYth | 6646 | 13 | Apath | -0.0728 | -0.1228 | -0.0978 | 0.0000 | 0.0128 |
| Child Fact | ACC CT | PDS ParYth | 6646 | 13 | Bpath | 0.0035 | -0.0581 | -0.0273 | 0.0825 | 0.0157 |
| Child Fact | ACC CT | PDS ParYth | 6646 | 13 | Direct | -0.0095 | -0.0601 | -0.0348 | 0.0070 | 0.0129 |
| Child Fact | ACC CT | PDS ParYth | 6646 | 13 | Indirect | 0.0057 | -0.0004 | 0.0027 | 0.0865 | 0.0016 |
| Child Fact | ACC CT | PDS ParYth | 6646 | 13 | Total | -0.0070 | -0.0573 | -0.0322 | 0.0122 | 0.0128 |
| Child Fact | ACC CT | PDS Par | 6646 | 13 | Apath | -0.0270 | -0.0690 | -0.0480 | 0.0000 | 0.0107 |
| Child Fact | ACC CT | PDS Par | 6646 | 13 | Bpath | -0.0188 | -0.0770 | -0.0479 | 0.0013 | 0.0148 |
| Child Fact | ACC CT | PDS Par | 6646 | 13 | Direct | -0.0094 | -0.0595 | -0.0344 | 0.0070 | 0.0128 |
| Child Fact | ACC CT | PDS Par | 6646 | 13 | Indirect | 0.0040 | 0.0006 | 0.0023 | 0.0077 | 0.0009 |
| Child Fact | ACC CT | PDS Par | 6646 | 13 | Total | -0.0071 | -0.0572 | -0.0321 | 0.0118 | 0.0128 |
| Child Fact | ACC CT | PDS Yth | 6646 | 13 | Apath | -0.0812 | -0.1350 | -0.1081 | 0.0000 | 0.0137 |
| Child Fact | ACC CT | PDS Yth | 6646 | 13 | Bpath | 0.0246 | -0.0298 | -0.0026 | 0.8530 | 0.0139 |
| Child Fact | ACC CT | PDS Yth | 6646 | 13 | Direct | -0.0078 | -0.0571 | -0.0324 | 0.0100 | 0.0126 |
| Child Fact | ACC CT | PDS Yth | 6646 | 13 | Indirect | 0.0032 | -0.0027 | 0.0003 | 0.8530 | 0.0015 |
| Child Fact | ACC CT | PDS Yth | 6646 | 13 | Total | -0.0076 | -0.0567 | -0.0321 | 0.0102 | 0.0125 |
| Child Fact | Amygdala vol | PDS ParYth | 6646 | 13 | Apath | -0.0725 | -0.1232 | -0.0979 | 0.0000 | 0.0129 |
| Child Fact | Amygdala vol | PDS ParYth | 6646 | 13 | Bpath | 0.0593 | -0.0026 | 0.0284 | 0.0726 | 0.0158 |
| Child Fact | Amygdala vol | PDS ParYth | 6646 | 13 | Direct | 0.0345 | -0.0147 | 0.0099 | 0.4301 | 0.0126 |
| Child Fact | Amygdala vol | PDS ParYth | 6646 | 13 | Indirect | 0.0004 | -0.0059 | -0.0028 | 0.0844 | 0.0016 |
| Child Fact | Amygdala vol | PDS ParYth | 6646 | 13 | Total | 0.0316 | -0.0173 | 0.0071 | 0.5667 | 0.0125 |
| Child Fact | Amygdala vol | PDS Par | 6646 | 13 | Apath | -0.0262 | -0.0697 | -0.0479 | 0.0000 | 0.0111 |
| Child Fact | Amygdala vol | PDS Par | 6646 | 13 | Bpath | 0.0544 | -0.0025 | 0.0259 | 0.0742 | 0.0145 |
| Child Fact | Amygdala vol | PDS Par | 6646 | 13 | Direct | 0.0330 | -0.0163 | 0.0083 | 0.5071 | 0.0126 |
| Child Fact | Amygdala vol | PDS Par | 6646 | 13 | Indirect | 0.0003 | -0.0027 | -0.0012 | 0.1045 | 0.0008 |
| Child Fact | Amygdala vol | PDS Par | 6646 | 13 | Total | 0.0317 | -0.0175 | 0.0071 | 0.5717 | 0.0126 |
| Child Fact | Amygdala vol | PDS Yth | 6646 | 13 | Apath | -0.0815 | -0.1349 | -0.1082 | 0.0000 | 0.0136 |
| Child Fact | Amygdala vol | PDS Yth | 6646 | 13 | Bpath | 0.0382 | -0.0166 | 0.0108 | 0.4399 | 0.0140 |
| Child Fact | Amygdala vol | PDS Yth | 6646 | 13 | Direct | 0.0328 | -0.0163 | 0.0083 | 0.5082 | 0.0125 |
| Child Fact | Amygdala vol | PDS Yth | 6646 | 13 | Indirect | 0.0018 | -0.0041 | -0.0012 | 0.4419 | 0.0015 |
| Child Fact | Amygdala vol | PDS Yth | 6646 | 13 | Total | 0.0315 | -0.0172 | 0.0071 | 0.5669 | 0.0124 |
| Child Fact | L AmygCON | PDS ParYth | 6646 | 13 | Apath | -0.0732 | -0.1227 | -0.0980 | 0.0000 | 0.0126 |
| Child Fact | L AmygCON | PDS ParYth | 6646 | 13 | Bpath | -0.0314 | -0.0955 | -0.0635 | 0.0001 | 0.0164 |
| Child Fact | L AmygCON | PDS ParYth | 6646 | 13 | Direct | 0.0623 | 0.0134 | 0.0378 | 0.0024 | 0.0125 |
| Child Fact | L AmygCON | PDS ParYth | 6646 | 13 | Indirect | 0.0098 | 0.0027 | 0.0062 | 0.0006 | 0.0018 |
| Child Fact | L AmygCON | PDS ParYth | 6646 | 13 | Total | 0.0681 | 0.0200 | 0.0441 | 0.0003 | 0.0123 |
| Child Fact | L AmygCON | PDS Par | 6646 | 13 | Apath | -0.0259 | -0.0701 | -0.0480 | 0.0000 | 0.0113 |
| Child Fact | L AmygCON | PDS Par | 6646 | 13 | Bpath | -0.0590 | -0.1177 | -0.0883 | 0.0000 | 0.0150 |
| Child Fact | L AmygCON | PDS Par | 6646 | 13 | Direct | 0.0638 | 0.0160 | 0.0399 | 0.0011 | 0.0122 |
| Child Fact | L AmygCON | PDS Par | 6646 | 13 | Indirect | 0.0066 | 0.0018 | 0.0042 | 0.0005 | 0.0012 |
| Child Fact | L AmygCON | PDS Par | 6646 | 13 | Total | 0.0680 | 0.0203 | 0.0441 | 0.0003 | 0.0122 |
| Child Fact | L AmygCON | PDS Yth | 6646 | 13 | Apath | -0.0815 | -0.1348 | -0.1081 | 0.0000 | 0.0136 |
| Child Fact | L AmygCON | PDS Yth | 6646 | 13 | Bpath | 0.0046 | -0.0542 | -0.0248 | 0.0987 | 0.0150 |
| Child Fact | L AmygCON | PDS Yth | 6646 | 13 | Direct | 0.0660 | 0.0166 | 0.0413 | 0.0010 | 0.0126 |
| Child Fact | L AmygCON | PDS Yth | 6646 | 13 | Indirect | 0.0059 | -0.0006 | 0.0027 | 0.1047 | 0.0017 |
| Child Fact | L AmygCON | PDS Yth | 6646 | 13 | Total | 0.0684 | 0.0195 | 0.0440 | 0.0004 | 0.0125 |
| Child Fact | R AmygCON | PDS ParYth | 6646 | 13 | Apath | -0.0733 | -0.1228 | -0.0980 | 0.0000 | 0.0126 |
| Child Fact | R AmygCON | PDS ParYth | 6646 | 13 | Bpath | -0.0256 | -0.0877 | -0.0567 | 0.0003 | 0.0158 |
| Child Fact | R AmygCON | PDS ParYth | 6646 | 13 | Direct | 0.0614 | 0.0100 | 0.0357 | 0.0065 | 0.0131 |
| Child Fact | R AmygCON | PDS ParYth | 6646 | 13 | Indirect | 0.0089 | 0.0022 | 0.0056 | 0.0013 | 0.0017 |
| Child Fact | R AmygCON | PDS ParYth | 6646 | 13 | Total | 0.0667 | 0.0159 | 0.0413 | 0.0014 | 0.0130 |
| Child Fact | R AmygCON | PDS Par | 6646 | 13 | Apath | -0.0266 | -0.0696 | -0.0481 | 0.0000 | 0.0110 |
| Child Fact | R AmygCON | PDS Par | 6646 | 13 | Bpath | -0.0486 | -0.1064 | -0.0775 | 0.0000 | 0.0148 |
| Child Fact | R AmygCON | PDS Par | 6646 | 13 | Direct | 0.0625 | 0.0127 | 0.0376 | 0.0030 | 0.0127 |
| Child Fact | R AmygCON | PDS Par | 6646 | 13 | Indirect | 0.0059 | 0.0015 | 0.0037 | 0.0009 | 0.0011 |
| Child Fact | R AmygCON | PDS Par | 6646 | 13 | Total | 0.0662 | 0.0165 | 0.0414 | 0.0011 | 0.0127 |
| Child Fact | R AmygCON | PDS Yth | 6646 | 13 | Apath | -0.0815 | -0.1349 | -0.1082 | 0.0000 | 0.0136 |
| Child Fact | R AmygCON | PDS Yth | 6646 | 13 | Bpath | 0.0070 | -0.0508 | -0.0219 | 0.1373 | 0.0148 |
| Child Fact | R AmygCON | PDS Yth | 6646 | 13 | Direct | 0.0639 | 0.0138 | 0.0388 | 0.0024 | 0.0128 |
| Child Fact | R AmygCON | PDS Yth | 6646 | 13 | Indirect | 0.0056 | -0.0008 | 0.0024 | 0.1467 | 0.0016 |
| Child Fact | R AmygCON | PDS Yth | 6646 | 13 | Total | 0.0660 | 0.0164 | 0.0412 | 0.0011 | 0.0126 |
| Par Accept | ACC CA | PDS ParYth | 6633 | 13 | Apath | -0.0469 | -0.0968 | -0.0718 | 0.0000 | 0.0127 |
| Par Accept | ACC CA | PDS ParYth | 6633 | 13 | Bpath | 0.0465 | -0.0133 | 0.0166 | 0.2768 | 0.0153 |
| Par Accept | ACC CA | PDS ParYth | 6633 | 13 | Direct | 0.0617 | 0.0143 | 0.0380 | 0.0017 | 0.0121 |
| Par Accept | ACC CA | PDS ParYth | 6633 | 13 | Indirect | 0.0010 | -0.0034 | -0.0012 | 0.2871 | 0.0011 |
| Par Accept | ACC CA | PDS ParYth | 6633 | 13 | Total | 0.0605 | 0.0132 | 0.0368 | 0.0023 | 0.0121 |
| Par Accept | ACC CA | PDS Par | 6633 | 13 | Apath | -0.0209 | -0.0638 | -0.0423 | 0.0001 | 0.0110 |
| Par Accept | ACC CA | PDS Par | 6633 | 13 | Bpath | 0.0702 | 0.0121 | 0.0412 | 0.0055 | 0.0148 |
| Par Accept | ACC CA | PDS Par | 6633 | 13 | Direct | 0.0619 | 0.0153 | 0.0386 | 0.0011 | 0.0119 |
| Par Accept | ACC CA | PDS Par | 6633 | 13 | Indirect | -0.0002 | -0.0033 | -0.0017 | 0.0252 | 0.0008 |
| Par Accept | ACC CA | PDS Par | 6633 | 13 | Total | 0.0601 | 0.0136 | 0.0369 | 0.0019 | 0.0119 |
| Par Accept | ACC CA | PDS Yth | 6633 | 13 | Apath | -0.0387 | -0.0920 | -0.0654 | 0.0000 | 0.0136 |
| Par Accept | ACC CA | PDS Yth | 6633 | 13 | Bpath | 0.0270 | -0.0274 | -0.0002 | 0.9892 | 0.0139 |
| Par Accept | ACC CA | PDS Yth | 6633 | 13 | Direct | 0.0606 | 0.0130 | 0.0368 | 0.0024 | 0.0121 |
| Par Accept | ACC CA | PDS Yth | 6633 | 13 | Indirect | 0.0018 | -0.0018 | 0.0000 | 0.9892 | 0.0009 |
| Par Accept | ACC CA | PDS Yth | 6633 | 13 | Total | 0.0605 | 0.0131 | 0.0368 | 0.0023 | 0.0121 |
| Par Accept | ACC CT | PDS ParYth | 6633 | 13 | Apath | -0.0461 | -0.0971 | -0.0716 | 0.0000 | 0.0130 |
| Par Accept | ACC CT | PDS ParYth | 6633 | 13 | Bpath | 0.0083 | -0.0539 | -0.0228 | 0.1514 | 0.0159 |
| Par Accept | ACC CT | PDS ParYth | 6633 | 13 | Direct | 0.0094 | -0.0403 | -0.0155 | 0.2233 | 0.0127 |
| Par Accept | ACC CT | PDS ParYth | 6633 | 13 | Indirect | 0.0039 | -0.0006 | 0.0016 | 0.1579 | 0.0012 |
| Par Accept | ACC CT | PDS ParYth | 6633 | 13 | Total | 0.0109 | -0.0386 | -0.0138 | 0.2737 | 0.0126 |
| Par Accept | ACC CT | PDS Par | 6633 | 13 | Apath | -0.0207 | -0.0638 | -0.0422 | 0.0001 | 0.0110 |
| Par Accept | ACC CT | PDS Par | 6633 | 13 | Bpath | -0.0162 | -0.0747 | -0.0454 | 0.0023 | 0.0149 |
| Par Accept | ACC CT | PDS Par | 6633 | 13 | Direct | 0.0089 | -0.0405 | -0.0158 | 0.2108 | 0.0126 |
| Par Accept | ACC CT | PDS Par | 6633 | 13 | Indirect | 0.0035 | 0.0004 | 0.0019 | 0.0164 | 0.0008 |
| Par Accept | ACC CT | PDS Par | 6633 | 13 | Total | 0.0108 | -0.0385 | -0.0139 | 0.2710 | 0.0126 |
| Par Accept | ACC CT | PDS Yth | 6633 | 13 | Apath | -0.0381 | -0.0926 | -0.0654 | 0.0000 | 0.0139 |
| Par Accept | ACC CT | PDS Yth | 6633 | 13 | Bpath | 0.0288 | -0.0259 | 0.0014 | 0.9174 | 0.0140 |
| Par Accept | ACC CT | PDS Yth | 6633 | 13 | Direct | 0.0112 | -0.0386 | -0.0137 | 0.2809 | 0.0127 |
| Par Accept | ACC CT | PDS Yth | 6633 | 13 | Indirect | 0.0017 | -0.0019 | -0.0001 | 0.9175 | 0.0009 |
| Par Accept | ACC CT | PDS Yth | 6633 | 13 | Total | 0.0110 | -0.0386 | -0.0138 | 0.2761 | 0.0127 |
| Par Accept | Amygdala vol | PDS ParYth | 6633 | 13 | Apath | -0.0475 | -0.0959 | -0.0717 | 0.0000 | 0.0123 |
| Par Accept | Amygdala vol | PDS ParYth | 6633 | 13 | Bpath | 0.0563 | -0.0047 | 0.0258 | 0.0974 | 0.0156 |
| Par Accept | Amygdala vol | PDS ParYth | 6633 | 13 | Direct | 0.0364 | -0.0125 | 0.0119 | 0.3377 | 0.0125 |
| Par Accept | Amygdala vol | PDS ParYth | 6633 | 13 | Indirect | 0.0005 | -0.0042 | -0.0018 | 0.1182 | 0.0012 |
| Par Accept | Amygdala vol | PDS ParYth | 6633 | 13 | Total | 0.0344 | -0.0142 | 0.0101 | 0.4155 | 0.0124 |
| Par Accept | Amygdala vol | PDS Par | 6633 | 13 | Apath | -0.0207 | -0.0637 | -0.0422 | 0.0001 | 0.0110 |
| Par Accept | Amygdala vol | PDS Par | 6633 | 13 | Bpath | 0.0523 | -0.0039 | 0.0242 | 0.0921 | 0.0143 |
| Par Accept | Amygdala vol | PDS Par | 6633 | 13 | Direct | 0.0356 | -0.0133 | 0.0111 | 0.3724 | 0.0125 |
| Par Accept | Amygdala vol | PDS Par | 6633 | 13 | Indirect | 0.0003 | -0.0023 | -0.0010 | 0.1297 | 0.0007 |
| Par Accept | Amygdala vol | PDS Par | 6633 | 13 | Total | 0.0346 | -0.0143 | 0.0101 | 0.4178 | 0.0125 |
| Par Accept | Amygdala vol | PDS Yth | 6633 | 13 | Apath | -0.0383 | -0.0925 | -0.0654 | 0.0000 | 0.0138 |
| Par Accept | Amygdala vol | PDS Yth | 6633 | 13 | Bpath | 0.0358 | -0.0180 | 0.0089 | 0.5179 | 0.0137 |
| Par Accept | Amygdala vol | PDS Yth | 6633 | 13 | Direct | 0.0347 | -0.0134 | 0.0107 | 0.3856 | 0.0123 |
| Par Accept | Amygdala vol | PDS Yth | 6633 | 13 | Indirect | 0.0012 | -0.0024 | -0.0006 | 0.5211 | 0.0009 |
| Par Accept | Amygdala vol | PDS Yth | 6633 | 13 | Total | 0.0341 | -0.0139 | 0.0101 | 0.4107 | 0.0123 |
| Par Accept | L AmygCON | PDS ParYth | 6633 | 13 | Apath | -0.0464 | -0.0967 | -0.0716 | 0.0000 | 0.0128 |
| Par Accept | L AmygCON | PDS ParYth | 6633 | 13 | Bpath | -0.0341 | -0.0969 | -0.0655 | 0.0000 | 0.0160 |
| Par Accept | L AmygCON | PDS ParYth | 6633 | 13 | Direct | 0.0479 | 0.0009 | 0.0244 | 0.0418 | 0.0120 |
| Par Accept | L AmygCON | PDS ParYth | 6633 | 13 | Indirect | 0.0075 | 0.0019 | 0.0047 | 0.0012 | 0.0014 |
| Par Accept | L AmygCON | PDS ParYth | 6633 | 13 | Total | 0.0524 | 0.0058 | 0.0291 | 0.0144 | 0.0119 |
| Par Accept | L AmygCON | PDS Par | 6633 | 13 | Apath | -0.0207 | -0.0634 | -0.0420 | 0.0001 | 0.0109 |
| Par Accept | L AmygCON | PDS Par | 6633 | 13 | Bpath | -0.0613 | -0.1203 | -0.0908 | 0.0000 | 0.0151 |
| Par Accept | L AmygCON | PDS Par | 6633 | 13 | Direct | 0.0487 | 0.0015 | 0.0251 | 0.0371 | 0.0120 |
| Par Accept | L AmygCON | PDS Par | 6633 | 13 | Indirect | 0.0061 | 0.0015 | 0.0038 | 0.0012 | 0.0012 |
| Par Accept | L AmygCON | PDS Par | 6633 | 13 | Total | 0.0525 | 0.0054 | 0.0289 | 0.0161 | 0.0120 |
| Par Accept | L AmygCON | PDS Yth | 6633 | 13 | Apath | -0.0381 | -0.0926 | -0.0654 | 0.0000 | 0.0139 |
| Par Accept | L AmygCON | PDS Yth | 6633 | 13 | Bpath | 0.0017 | -0.0558 | -0.0270 | 0.0654 | 0.0147 |
| Par Accept | L AmygCON | PDS Yth | 6633 | 13 | Direct | 0.0505 | 0.0043 | 0.0274 | 0.0202 | 0.0118 |
| Par Accept | L AmygCON | PDS Yth | 6633 | 13 | Indirect | 0.0038 | -0.0003 | 0.0018 | 0.0895 | 0.0010 |
| Par Accept | L AmygCON | PDS Yth | 6633 | 13 | Total | 0.0522 | 0.0062 | 0.0292 | 0.0129 | 0.0117 |
| Par Accept | R AmygCON | PDS ParYth | 6633 | 13 | Apath | -0.0469 | -0.0966 | -0.0717 | 0.0000 | 0.0127 |
| Par Accept | R AmygCON | PDS ParYth | 6633 | 13 | Bpath | -0.0262 | -0.0901 | -0.0581 | 0.0004 | 0.0163 |
| Par Accept | R AmygCON | PDS ParYth | 6633 | 13 | Direct | 0.0477 | -0.0015 | 0.0231 | 0.0651 | 0.0126 |
| Par Accept | R AmygCON | PDS ParYth | 6633 | 13 | Indirect | 0.0069 | 0.0015 | 0.0042 | 0.0025 | 0.0014 |
| Par Accept | R AmygCON | PDS ParYth | 6633 | 13 | Total | 0.0517 | 0.0030 | 0.0273 | 0.0279 | 0.0124 |
| Par Accept | R AmygCON | PDS Par | 6633 | 13 | Apath | -0.0204 | -0.0639 | -0.0422 | 0.0001 | 0.0111 |
| Par Accept | R AmygCON | PDS Par | 6633 | 13 | Bpath | -0.0499 | -0.1076 | -0.0787 | 0.0000 | 0.0147 |
| Par Accept | R AmygCON | PDS Par | 6633 | 13 | Direct | 0.0482 | -0.0005 | 0.0239 | 0.0548 | 0.0124 |
| Par Accept | R AmygCON | PDS Par | 6633 | 13 | Indirect | 0.0054 | 0.0012 | 0.0033 | 0.0020 | 0.0011 |
| Par Accept | R AmygCON | PDS Par | 6633 | 13 | Total | 0.0514 | 0.0029 | 0.0272 | 0.0280 | 0.0124 |
| Par Accept | R AmygCON | PDS Yth | 6633 | 13 | Apath | -0.0382 | -0.0926 | -0.0654 | 0.0000 | 0.0139 |
| Par Accept | R AmygCON | PDS Yth | 6633 | 13 | Bpath | 0.0045 | -0.0518 | -0.0237 | 0.0991 | 0.0144 |
| Par Accept | R AmygCON | PDS Yth | 6633 | 13 | Direct | 0.0504 | 0.0014 | 0.0259 | 0.0386 | 0.0125 |
| Par Accept | R AmygCON | PDS Yth | 6633 | 13 | Indirect | 0.0035 | -0.0004 | 0.0015 | 0.1224 | 0.0010 |
| Par Accept | R AmygCON | PDS Yth | 6633 | 13 | Total | 0.0518 | 0.0030 | 0.0274 | 0.0275 | 0.0124 |
| Demo Fact | ACC CA | PDS ParYth | 6658 | 13 | Apath | -0.1998 | -0.2487 | -0.2242 | 0.0000 | 0.0125 |
| Demo Fact | ACC CA | PDS ParYth | 6658 | 13 | Bpath | 0.0361 | -0.0271 | 0.0045 | 0.7817 | 0.0161 |
| Demo Fact | ACC CA | PDS ParYth | 6658 | 13 | Direct | -0.0155 | -0.0670 | -0.0413 | 0.0017 | 0.0131 |
| Demo Fact | ACC CA | PDS ParYth | 6658 | 13 | Indirect | 0.0061 | -0.0081 | -0.0010 | 0.7817 | 0.0036 |
| Demo Fact | ACC CA | PDS ParYth | 6658 | 13 | Total | -0.0175 | -0.0670 | -0.0423 | 0.0008 | 0.0126 |
| Demo Fact | ACC CA | PDS Par | 6658 | 13 | Apath | -0.2009 | -0.2441 | -0.2225 | 0.0000 | 0.0110 |
| Demo Fact | ACC CA | PDS Par | 6658 | 13 | Bpath | 0.0577 | -0.0016 | 0.0281 | 0.0633 | 0.0151 |
| Demo Fact | ACC CA | PDS Par | 6658 | 13 | Direct | -0.0098 | -0.0622 | -0.0360 | 0.0070 | 0.0134 |
| Demo Fact | ACC CA | PDS Par | 6658 | 13 | Indirect | 0.0004 | -0.0129 | -0.0062 | 0.0645 | 0.0034 |
| Demo Fact | ACC CA | PDS Par | 6658 | 13 | Total | -0.0169 | -0.0677 | -0.0423 | 0.0011 | 0.0130 |
| Demo Fact | ACC CA | PDS Yth | 6658 | 13 | Apath | -0.1371 | -0.1901 | -0.1636 | 0.0000 | 0.0135 |
| Demo Fact | ACC CA | PDS Yth | 6658 | 13 | Bpath | 0.0200 | -0.0350 | -0.0075 | 0.5921 | 0.0140 |
| Demo Fact | ACC CA | PDS Yth | 6658 | 13 | Direct | -0.0175 | -0.0695 | -0.0435 | 0.0010 | 0.0133 |
| Demo Fact | ACC CA | PDS Yth | 6658 | 13 | Indirect | 0.0057 | -0.0033 | 0.0012 | 0.5924 | 0.0023 |
| Demo Fact | ACC CA | PDS Yth | 6658 | 13 | Total | -0.0168 | -0.0677 | -0.0423 | 0.0011 | 0.0130 |
| Demo Fact | ACC CT | PDS ParYth | 6658 | 13 | Apath | -0.1999 | -0.2485 | -0.2242 | 0.0000 | 0.0124 |
| Demo Fact | ACC CT | PDS ParYth | 6658 | 13 | Bpath | 0.0093 | -0.0535 | -0.0221 | 0.1683 | 0.0160 |
| Demo Fact | ACC CT | PDS ParYth | 6658 | 13 | Direct | 0.0298 | -0.0239 | 0.0029 | 0.8301 | 0.0137 |
| Demo Fact | ACC CT | PDS ParYth | 6658 | 13 | Indirect | 0.0120 | -0.0021 | 0.0050 | 0.1691 | 0.0036 |
| Demo Fact | ACC CT | PDS ParYth | 6658 | 13 | Total | 0.0336 | -0.0178 | 0.0079 | 0.5470 | 0.0131 |
| Demo Fact | ACC CT | PDS Par | 6658 | 13 | Apath | -0.2008 | -0.2440 | -0.2224 | 0.0000 | 0.0110 |
| Demo Fact | ACC CT | PDS Par | 6658 | 13 | Bpath | -0.0146 | -0.0752 | -0.0449 | 0.0037 | 0.0154 |
| Demo Fact | ACC CT | PDS Par | 6658 | 13 | Direct | 0.0245 | -0.0287 | -0.0021 | 0.8775 | 0.0136 |
| Demo Fact | ACC CT | PDS Par | 6658 | 13 | Indirect | 0.0168 | 0.0032 | 0.0100 | 0.0041 | 0.0035 |
| Demo Fact | ACC CT | PDS Par | 6658 | 13 | Total | 0.0335 | -0.0178 | 0.0079 | 0.5466 | 0.0131 |
| Demo Fact | ACC CT | PDS Yth | 6658 | 13 | Apath | -0.1368 | -0.1905 | -0.1636 | 0.0000 | 0.0137 |
| Demo Fact | ACC CT | PDS Yth | 6658 | 13 | Bpath | 0.0291 | -0.0257 | 0.0017 | 0.9045 | 0.0140 |
| Demo Fact | ACC CT | PDS Yth | 6658 | 13 | Direct | 0.0345 | -0.0181 | 0.0082 | 0.5424 | 0.0134 |
| Demo Fact | ACC CT | PDS Yth | 6658 | 13 | Indirect | 0.0042 | -0.0048 | -0.0003 | 0.9045 | 0.0023 |
| Demo Fact | ACC CT | PDS Yth | 6658 | 13 | Total | 0.0336 | -0.0178 | 0.0079 | 0.5477 | 0.0131 |
| Demo Fact | Amygdala vol | PDS ParYth | 6658 | 13 | Apath | -0.2000 | -0.2487 | -0.2243 | 0.0000 | 0.0124 |
| Demo Fact | Amygdala vol | PDS ParYth | 6658 | 13 | Bpath | 0.0618 | -0.0013 | 0.0303 | 0.0601 | 0.0161 |
| Demo Fact | Amygdala vol | PDS ParYth | 6658 | 13 | Direct | 0.0344 | -0.0176 | 0.0084 | 0.5282 | 0.0133 |
| Demo Fact | Amygdala vol | PDS ParYth | 6658 | 13 | Indirect | 0.0003 | -0.0139 | -0.0068 | 0.0615 | 0.0036 |
| Demo Fact | Amygdala vol | PDS ParYth | 6658 | 13 | Total | 0.0266 | -0.0234 | 0.0016 | 0.9015 | 0.0128 |
| Demo Fact | Amygdala vol | PDS Par | 6658 | 13 | Apath | -0.2006 | -0.2445 | -0.2225 | 0.0000 | 0.0112 |
| Demo Fact | Amygdala vol | PDS Par | 6658 | 13 | Bpath | 0.0571 | -0.0008 | 0.0281 | 0.0568 | 0.0148 |
| Demo Fact | Amygdala vol | PDS Par | 6658 | 13 | Direct | 0.0332 | -0.0175 | 0.0078 | 0.5449 | 0.0129 |
| Demo Fact | Amygdala vol | PDS Par | 6658 | 13 | Indirect | 0.0002 | -0.0127 | -0.0063 | 0.0577 | 0.0033 |
| Demo Fact | Amygdala vol | PDS Par | 6658 | 13 | Total | 0.0263 | -0.0232 | 0.0016 | 0.9008 | 0.0126 |
| Demo Fact | Amygdala vol | PDS Yth | 6658 | 13 | Apath | -0.1370 | -0.1905 | -0.1637 | 0.0000 | 0.0136 |
| Demo Fact | Amygdala vol | PDS Yth | 6658 | 13 | Bpath | 0.0379 | -0.0172 | 0.0104 | 0.4613 | 0.0141 |
| Demo Fact | Amygdala vol | PDS Yth | 6658 | 13 | Direct | 0.0285 | -0.0220 | 0.0033 | 0.8000 | 0.0129 |
| Demo Fact | Amygdala vol | PDS Yth | 6658 | 13 | Indirect | 0.0028 | -0.0062 | -0.0017 | 0.4613 | 0.0023 |
| Demo Fact | Amygdala vol | PDS Yth | 6658 | 13 | Total | 0.0263 | -0.0232 | 0.0016 | 0.9015 | 0.0126 |
| Demo Fact | L AmygCON | PDS ParYth | 6658 | 13 | Apath | -0.1994 | -0.2493 | -0.2244 | 0.0000 | 0.0127 |
| Demo Fact | L AmygCON | PDS ParYth | 6658 | 13 | Bpath | -0.0007 | -0.0655 | -0.0331 | 0.0455 | 0.0165 |
| Demo Fact | L AmygCON | PDS ParYth | 6658 | 13 | Direct | 0.1658 | 0.1113 | 0.1385 | 0.0000 | 0.0139 |
| Demo Fact | L AmygCON | PDS ParYth | 6658 | 13 | Indirect | 0.0148 | 0.0000 | 0.0074 | 0.0487 | 0.0038 |
| Demo Fact | L AmygCON | PDS ParYth | 6658 | 13 | Total | 0.1719 | 0.1200 | 0.1460 | 0.0000 | 0.0132 |
| Demo Fact | L AmygCON | PDS Par | 6658 | 13 | Apath | -0.2013 | -0.2441 | -0.2227 | 0.0000 | 0.0109 |
| Demo Fact | L AmygCON | PDS Par | 6658 | 13 | Bpath | -0.0254 | -0.0859 | -0.0556 | 0.0003 | 0.0154 |
| Demo Fact | L AmygCON | PDS Par | 6658 | 13 | Direct | 0.1601 | 0.1074 | 0.1338 | 0.0000 | 0.0135 |
| Demo Fact | L AmygCON | PDS Par | 6658 | 13 | Indirect | 0.0193 | 0.0055 | 0.0124 | 0.0004 | 0.0035 |
| Demo Fact | L AmygCON | PDS Par | 6658 | 13 | Total | 0.1716 | 0.1207 | 0.1462 | 0.0000 | 0.0130 |
| Demo Fact | L AmygCON | PDS Yth | 6658 | 13 | Apath | -0.1373 | -0.1900 | -0.1636 | 0.0000 | 0.0135 |
| Demo Fact | L AmygCON | PDS Yth | 6658 | 13 | Bpath | 0.0232 | -0.0360 | -0.0064 | 0.6719 | 0.0151 |
| Demo Fact | L AmygCON | PDS Yth | 6658 | 13 | Direct | 0.1709 | 0.1189 | 0.1449 | 0.0000 | 0.0133 |
| Demo Fact | L AmygCON | PDS Yth | 6658 | 13 | Indirect | 0.0059 | -0.0038 | 0.0010 | 0.6724 | 0.0025 |
| Demo Fact | L AmygCON | PDS Yth | 6658 | 13 | Total | 0.1713 | 0.1207 | 0.1460 | 0.0000 | 0.0129 |
| Demo Fact | R AmygCON | PDS ParYth | 6658 | 13 | Apath | -0.1999 | -0.2487 | -0.2243 | 0.0000 | 0.0125 |
| Demo Fact | R AmygCON | PDS ParYth | 6658 | 13 | Bpath | 0.0001 | -0.0630 | -0.0315 | 0.0505 | 0.0161 |
| Demo Fact | R AmygCON | PDS ParYth | 6658 | 13 | Direct | 0.1389 | 0.0850 | 0.1120 | 0.0000 | 0.0138 |
| Demo Fact | R AmygCON | PDS ParYth | 6658 | 13 | Indirect | 0.0142 | -0.0001 | 0.0071 | 0.0526 | 0.0036 |
| Demo Fact | R AmygCON | PDS ParYth | 6658 | 13 | Total | 0.1449 | 0.0931 | 0.1190 | 0.0000 | 0.0132 |
| Demo Fact | R AmygCON | PDS Par | 6658 | 13 | Apath | -0.2010 | -0.2442 | -0.2226 | 0.0000 | 0.0110 |
| Demo Fact | R AmygCON | PDS Par | 6658 | 13 | Bpath | -0.0218 | -0.0806 | -0.0512 | 0.0007 | 0.0150 |
| Demo Fact | R AmygCON | PDS Par | 6658 | 13 | Direct | 0.1345 | 0.0811 | 0.1078 | 0.0000 | 0.0136 |
| Demo Fact | R AmygCON | PDS Par | 6658 | 13 | Indirect | 0.0180 | 0.0048 | 0.0114 | 0.0008 | 0.0034 |
| Demo Fact | R AmygCON | PDS Par | 6658 | 13 | Total | 0.1450 | 0.0934 | 0.1192 | 0.0000 | 0.0132 |
| Demo Fact | R AmygCON | PDS Yth | 6658 | 13 | Apath | -0.1373 | -0.1900 | -0.1636 | 0.0000 | 0.0134 |
| Demo Fact | R AmygCON | PDS Yth | 6658 | 13 | Bpath | 0.0221 | -0.0354 | -0.0067 | 0.6485 | 0.0147 |
| Demo Fact | R AmygCON | PDS Yth | 6658 | 13 | Direct | 0.1441 | 0.0918 | 0.1179 | 0.0000 | 0.0133 |
| Demo Fact | R AmygCON | PDS Yth | 6658 | 13 | Indirect | 0.0058 | -0.0036 | 0.0011 | 0.6489 | 0.0024 |
| Demo Fact | R AmygCON | PDS Yth | 6658 | 13 | Total | 0.1444 | 0.0936 | 0.1190 | 0.0000 | 0.0130 |
| FamEnv Fact | ACC CA | PDS ParYth | 6658 | 13 | Apath | -0.1600 | -0.2069 | -0.1835 | 0.0000 | 0.0120 |
| FamEnv Fact | ACC CA | PDS ParYth | 6658 | 13 | Bpath | 0.0417 | -0.0196 | 0.0110 | 0.4803 | 0.0156 |
| FamEnv Fact | ACC CA | PDS ParYth | 6658 | 13 | Direct | 0.0172 | -0.0331 | -0.0079 | 0.5379 | 0.0128 |
| FamEnv Fact | ACC CA | PDS ParYth | 6658 | 13 | Indirect | 0.0036 | -0.0077 | -0.0020 | 0.4807 | 0.0029 |
| FamEnv Fact | ACC CA | PDS ParYth | 6658 | 13 | Total | 0.0144 | -0.0343 | -0.0099 | 0.4239 | 0.0124 |
| FamEnv Fact | ACC CA | PDS Par | 6658 | 13 | Apath | -0.1421 | -0.1834 | -0.1627 | 0.0000 | 0.0105 |
| FamEnv Fact | ACC CA | PDS Par | 6658 | 13 | Bpath | 0.0663 | 0.0074 | 0.0369 | 0.0142 | 0.0150 |
| FamEnv Fact | ACC CA | PDS Par | 6658 | 13 | Direct | 0.0209 | -0.0288 | -0.0040 | 0.7541 | 0.0127 |
| FamEnv Fact | ACC CA | PDS Par | 6658 | 13 | Indirect | -0.0011 | -0.0109 | -0.0060 | 0.0157 | 0.0025 |
| FamEnv Fact | ACC CA | PDS Par | 6658 | 13 | Total | 0.0144 | -0.0344 | -0.0100 | 0.4235 | 0.0125 |
| FamEnv Fact | ACC CA | PDS Yth | 6658 | 13 | Apath | -0.1214 | -0.1719 | -0.1467 | 0.0000 | 0.0129 |
| FamEnv Fact | ACC CA | PDS Yth | 6658 | 13 | Bpath | 0.0241 | -0.0306 | -0.0032 | 0.8162 | 0.0140 |
| FamEnv Fact | ACC CA | PDS Yth | 6658 | 13 | Direct | 0.0145 | -0.0353 | -0.0104 | 0.4125 | 0.0127 |
| FamEnv Fact | ACC CA | PDS Yth | 6658 | 13 | Indirect | 0.0045 | -0.0035 | 0.0005 | 0.8163 | 0.0021 |
| FamEnv Fact | ACC CA | PDS Yth | 6658 | 13 | Total | 0.0146 | -0.0345 | -0.0099 | 0.4275 | 0.0125 |
| FamEnv Fact | ACC CT | PDS ParYth | 6658 | 13 | Apath | -0.1598 | -0.2068 | -0.1833 | 0.0000 | 0.0120 |
| FamEnv Fact | ACC CT | PDS ParYth | 6658 | 13 | Bpath | 0.0070 | -0.0562 | -0.0246 | 0.1276 | 0.0161 |
| FamEnv Fact | ACC CT | PDS ParYth | 6658 | 13 | Direct | 0.0100 | -0.0416 | -0.0158 | 0.2309 | 0.0132 |
| FamEnv Fact | ACC CT | PDS ParYth | 6658 | 13 | Indirect | 0.0103 | -0.0013 | 0.0045 | 0.1279 | 0.0030 |
| FamEnv Fact | ACC CT | PDS ParYth | 6658 | 13 | Total | 0.0139 | -0.0364 | -0.0113 | 0.3803 | 0.0128 |
| FamEnv Fact | ACC CT | PDS Par | 6658 | 13 | Apath | -0.1423 | -0.1829 | -0.1626 | 0.0000 | 0.0104 |
| FamEnv Fact | ACC CT | PDS Par | 6658 | 13 | Bpath | -0.0188 | -0.0787 | -0.0487 | 0.0014 | 0.0153 |
| FamEnv Fact | ACC CT | PDS Par | 6658 | 13 | Direct | 0.0068 | -0.0451 | -0.0191 | 0.1481 | 0.0132 |
| FamEnv Fact | ACC CT | PDS Par | 6658 | 13 | Indirect | 0.0128 | 0.0030 | 0.0079 | 0.0016 | 0.0025 |
| FamEnv Fact | ACC CT | PDS Par | 6658 | 13 | Total | 0.0142 | -0.0366 | -0.0112 | 0.3877 | 0.0130 |
| FamEnv Fact | ACC CT | PDS Yth | 6658 | 13 | Apath | -0.1210 | -0.1723 | -0.1467 | 0.0000 | 0.0131 |
| FamEnv Fact | ACC CT | PDS Yth | 6658 | 13 | Bpath | 0.0272 | -0.0271 | 0.0000 | 0.9987 | 0.0139 |
| FamEnv Fact | ACC CT | PDS Yth | 6658 | 13 | Direct | 0.0144 | -0.0369 | -0.0112 | 0.3900 | 0.0131 |
| FamEnv Fact | ACC CT | PDS Yth | 6658 | 13 | Indirect | 0.0040 | -0.0040 | 0.0000 | 0.9987 | 0.0020 |
| FamEnv Fact | ACC CT | PDS Yth | 6658 | 13 | Total | 0.0140 | -0.0365 | -0.0112 | 0.3819 | 0.0129 |
| FamEnv Fact | Amygdala vol | PDS ParYth | 6658 | 13 | Apath | -0.1600 | -0.2072 | -0.1836 | 0.0000 | 0.0120 |
| FamEnv Fact | Amygdala vol | PDS ParYth | 6658 | 13 | Bpath | 0.0591 | -0.0032 | 0.0280 | 0.0789 | 0.0159 |
| FamEnv Fact | Amygdala vol | PDS ParYth | 6658 | 13 | Direct | 0.0248 | -0.0243 | 0.0002 | 0.9850 | 0.0125 |
| FamEnv Fact | Amygdala vol | PDS ParYth | 6658 | 13 | Indirect | 0.0007 | -0.0109 | -0.0051 | 0.0821 | 0.0030 |
| FamEnv Fact | Amygdala vol | PDS ParYth | 6658 | 13 | Total | 0.0189 | -0.0287 | -0.0049 | 0.6866 | 0.0121 |
| FamEnv Fact | Amygdala vol | PDS Par | 6658 | 13 | Apath | -0.1422 | -0.1833 | -0.1627 | 0.0000 | 0.0105 |
| FamEnv Fact | Amygdala vol | PDS Par | 6658 | 13 | Bpath | 0.0544 | -0.0036 | 0.0254 | 0.0857 | 0.0148 |
| FamEnv Fact | Amygdala vol | PDS Par | 6658 | 13 | Direct | 0.0239 | -0.0255 | -0.0008 | 0.9497 | 0.0126 |
| FamEnv Fact | Amygdala vol | PDS Par | 6658 | 13 | Indirect | 0.0006 | -0.0089 | -0.0041 | 0.0872 | 0.0024 |
| FamEnv Fact | Amygdala vol | PDS Par | 6658 | 13 | Total | 0.0193 | -0.0292 | -0.0049 | 0.6898 | 0.0124 |
| FamEnv Fact | Amygdala vol | PDS Yth | 6658 | 13 | Apath | -0.1211 | -0.1725 | -0.1468 | 0.0000 | 0.0131 |
| FamEnv Fact | Amygdala vol | PDS Yth | 6658 | 13 | Bpath | 0.0377 | -0.0189 | 0.0094 | 0.5153 | 0.0144 |
| FamEnv Fact | Amygdala vol | PDS Yth | 6658 | 13 | Direct | 0.0213 | -0.0283 | -0.0035 | 0.7803 | 0.0126 |
| FamEnv Fact | Amygdala vol | PDS Yth | 6658 | 13 | Indirect | 0.0028 | -0.0055 | -0.0014 | 0.5151 | 0.0021 |
| FamEnv Fact | Amygdala vol | PDS Yth | 6658 | 13 | Total | 0.0195 | -0.0293 | -0.0049 | 0.6931 | 0.0124 |
| FamEnv Fact | L AmygCON | PDS ParYth | 6658 | 13 | Apath | -0.1599 | -0.2072 | -0.1836 | 0.0000 | 0.0121 |
| FamEnv Fact | L AmygCON | PDS ParYth | 6658 | 13 | Bpath | -0.0131 | -0.0783 | -0.0457 | 0.0059 | 0.0166 |
| FamEnv Fact | L AmygCON | PDS ParYth | 6658 | 13 | Direct | 0.1243 | 0.0758 | 0.1000 | 0.0000 | 0.0124 |
| FamEnv Fact | L AmygCON | PDS ParYth | 6658 | 13 | Indirect | 0.0145 | 0.0023 | 0.0084 | 0.0070 | 0.0031 |
| FamEnv Fact | L AmygCON | PDS ParYth | 6658 | 13 | Total | 0.1320 | 0.0849 | 0.1084 | 0.0000 | 0.0120 |
| FamEnv Fact | L AmygCON | PDS Par | 6658 | 13 | Apath | -0.1424 | -0.1836 | -0.1630 | 0.0000 | 0.0105 |
| FamEnv Fact | L AmygCON | PDS Par | 6658 | 13 | Bpath | -0.0421 | -0.1012 | -0.0716 | 0.0000 | 0.0151 |
| FamEnv Fact | L AmygCON | PDS Par | 6658 | 13 | Direct | 0.1213 | 0.0728 | 0.0971 | 0.0000 | 0.0124 |
| FamEnv Fact | L AmygCON | PDS Par | 6658 | 13 | Indirect | 0.0168 | 0.0066 | 0.0117 | 0.0000 | 0.0026 |
| FamEnv Fact | L AmygCON | PDS Par | 6658 | 13 | Total | 0.1327 | 0.0849 | 0.1088 | 0.0000 | 0.0122 |
| FamEnv Fact | L AmygCON | PDS Yth | 6658 | 13 | Apath | -0.1211 | -0.1722 | -0.1467 | 0.0000 | 0.0130 |
| FamEnv Fact | L AmygCON | PDS Yth | 6658 | 13 | Bpath | 0.0163 | -0.0431 | -0.0134 | 0.3765 | 0.0151 |
| FamEnv Fact | L AmygCON | PDS Yth | 6658 | 13 | Direct | 0.1310 | 0.0818 | 0.1064 | 0.0000 | 0.0126 |
| FamEnv Fact | L AmygCON | PDS Yth | 6658 | 13 | Indirect | 0.0063 | -0.0024 | 0.0020 | 0.3796 | 0.0022 |
| FamEnv Fact | L AmygCON | PDS Yth | 6658 | 13 | Total | 0.1323 | 0.0844 | 0.1084 | 0.0000 | 0.0122 |
| FamEnv Fact | R AmygCON | PDS ParYth | 6658 | 13 | Apath | -0.1602 | -0.2067 | -0.1834 | 0.0000 | 0.0118 |
| FamEnv Fact | R AmygCON | PDS ParYth | 6658 | 13 | Bpath | -0.0107 | -0.0735 | -0.0421 | 0.0085 | 0.0160 |
| FamEnv Fact | R AmygCON | PDS ParYth | 6658 | 13 | Direct | 0.1027 | 0.0520 | 0.0774 | 0.0000 | 0.0129 |
| FamEnv Fact | R AmygCON | PDS ParYth | 6658 | 13 | Indirect | 0.0136 | 0.0019 | 0.0077 | 0.0098 | 0.0030 |
| FamEnv Fact | R AmygCON | PDS ParYth | 6658 | 13 | Total | 0.1097 | 0.0605 | 0.0851 | 0.0000 | 0.0126 |
| FamEnv Fact | R AmygCON | PDS Par | 6658 | 13 | Apath | -0.1423 | -0.1834 | -0.1628 | 0.0000 | 0.0105 |
| FamEnv Fact | R AmygCON | PDS Par | 6658 | 13 | Bpath | -0.0358 | -0.0940 | -0.0649 | 0.0000 | 0.0148 |
| FamEnv Fact | R AmygCON | PDS Par | 6658 | 13 | Direct | 0.0996 | 0.0501 | 0.0748 | 0.0000 | 0.0126 |
| FamEnv Fact | R AmygCON | PDS Par | 6658 | 13 | Indirect | 0.0155 | 0.0057 | 0.0106 | 0.0000 | 0.0025 |
| FamEnv Fact | R AmygCON | PDS Par | 6658 | 13 | Total | 0.1099 | 0.0609 | 0.0854 | 0.0000 | 0.0125 |
| FamEnv Fact | R AmygCON | PDS Yth | 6658 | 13 | Apath | -0.1210 | -0.1723 | -0.1466 | 0.0000 | 0.0131 |
| FamEnv Fact | R AmygCON | PDS Yth | 6658 | 13 | Bpath | 0.0161 | -0.0414 | -0.0127 | 0.3878 | 0.0147 |
| FamEnv Fact | R AmygCON | PDS Yth | 6658 | 13 | Direct | 0.1084 | 0.0580 | 0.0832 | 0.0000 | 0.0129 |
| FamEnv Fact | R AmygCON | PDS Yth | 6658 | 13 | Indirect | 0.0061 | -0.0024 | 0.0019 | 0.3900 | 0.0022 |
| FamEnv Fact | R AmygCON | PDS Yth | 6658 | 13 | Total | 0.1097 | 0.0604 | 0.0850 | 0.0000 | 0.0126 |
| FES Par | ACC CA | PDS ParYth | 6653 | 13 | Apath | -0.0068 | -0.0546 | -0.0307 | 0.0118 | 0.0122 |
| FES Par | ACC CA | PDS ParYth | 6653 | 13 | Bpath | 0.0176 | -0.0424 | -0.0124 | 0.4167 | 0.0153 |
| FES Par | ACC CA | PDS ParYth | 6653 | 13 | Direct | 0.0231 | -0.0265 | -0.0017 | 0.8917 | 0.0126 |
| FES Par | ACC CA | PDS ParYth | 6653 | 13 | Indirect | 0.0006 | -0.0013 | -0.0004 | 0.4362 | 0.0005 |
| FES Par | ACC CA | PDS ParYth | 6653 | 13 | Total | 0.0227 | -0.0269 | -0.0021 | 0.8679 | 0.0126 |
| FES Par | ACC CA | PDS Par | 6653 | 13 | Apath | -0.0223 | -0.0637 | -0.0430 | 0.0000 | 0.0106 |
| FES Par | ACC CA | PDS Par | 6653 | 13 | Bpath | -0.0096 | -0.0667 | -0.0382 | 0.0088 | 0.0146 |
| FES Par | ACC CA | PDS Par | 6653 | 13 | Direct | 0.0244 | -0.0254 | -0.0005 | 0.9675 | 0.0127 |
| FES Par | ACC CA | PDS Par | 6653 | 13 | Indirect | -0.0002 | -0.0031 | -0.0016 | 0.0287 | 0.0008 |
| FES Par | ACC CA | PDS Par | 6653 | 13 | Total | 0.0228 | -0.0271 | -0.0022 | 0.8652 | 0.0127 |
| FES Par | ACC CA | PDS Yth | 6653 | 13 | Apath | 0.0208 | -0.0315 | -0.0054 | 0.6867 | 0.0133 |
| FES Par | ACC CA | PDS Yth | 6653 | 13 | Bpath | 0.0287 | -0.0253 | 0.0017 | 0.9013 | 0.0138 |
| FES Par | ACC CA | PDS Yth | 6653 | 13 | Direct | 0.0223 | -0.0265 | -0.0021 | 0.8652 | 0.0125 |
| FES Par | ACC CA | PDS Yth | 6653 | 13 | Indirect | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| FES Par | ACC CA | PDS Yth | 6653 | 13 | Total | 0.0223 | -0.0265 | -0.0021 | 0.8658 | 0.0125 |
| FES Par | ACC CT | PDS ParYth | 6653 | 13 | Apath | -0.0064 | -0.0548 | -0.0306 | 0.0133 | 0.0124 |
| FES Par | ACC CT | PDS ParYth | 6653 | 13 | Bpath | 0.0538 | -0.0087 | 0.0226 | 0.1566 | 0.0159 |
| FES Par | ACC CT | PDS ParYth | 6653 | 13 | Direct | 0.0240 | -0.0251 | -0.0005 | 0.9654 | 0.0125 |
| FES Par | ACC CT | PDS ParYth | 6653 | 13 | Indirect | 0.0018 | -0.0004 | 0.0007 | 0.2135 | 0.0006 |
| FES Par | ACC CT | PDS ParYth | 6653 | 13 | Total | 0.0247 | -0.0244 | 0.0001 | 0.9906 | 0.0125 |
| FES Par | ACC CT | PDS Par | 6653 | 13 | Apath | -0.0224 | -0.0638 | -0.0431 | 0.0000 | 0.0106 |
| FES Par | ACC CT | PDS Par | 6653 | 13 | Bpath | 0.0747 | 0.0167 | 0.0457 | 0.0020 | 0.0148 |
| FES Par | ACC CT | PDS Par | 6653 | 13 | Direct | 0.0228 | -0.0264 | -0.0018 | 0.8889 | 0.0126 |
| FES Par | ACC CT | PDS Par | 6653 | 13 | Indirect | 0.0035 | 0.0004 | 0.0020 | 0.0131 | 0.0008 |
| FES Par | ACC CT | PDS Par | 6653 | 13 | Total | 0.0248 | -0.0244 | 0.0002 | 0.9861 | 0.0125 |
| FES Par | ACC CT | PDS Yth | 6653 | 13 | Apath | 0.0213 | -0.0321 | -0.0054 | 0.6935 | 0.0136 |
| FES Par | ACC CT | PDS Yth | 6653 | 13 | Bpath | 0.0265 | -0.0279 | -0.0007 | 0.9593 | 0.0139 |
| FES Par | ACC CT | PDS Yth | 6653 | 13 | Direct | 0.0242 | -0.0239 | 0.0002 | 0.9898 | 0.0123 |
| FES Par | ACC CT | PDS Yth | 6653 | 13 | Indirect | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
| FES Par | ACC CT | PDS Yth | 6653 | 13 | Total | 0.0242 | -0.0239 | 0.0002 | 0.9900 | 0.0123 |
| FES Par | Amygdala vol | PDS ParYth | 6653 | 13 | Apath | -0.0067 | -0.0551 | -0.0309 | 0.0122 | 0.0123 |
| FES Par | Amygdala vol | PDS ParYth | 6653 | 13 | Bpath | 0.0055 | -0.0552 | -0.0248 | 0.1084 | 0.0155 |
| FES Par | Amygdala vol | PDS ParYth | 6653 | 13 | Direct | 0.0080 | -0.0386 | -0.0153 | 0.1973 | 0.0119 |
| FES Par | Amygdala vol | PDS ParYth | 6653 | 13 | Indirect | 0.0003 | -0.0019 | -0.0008 | 0.1763 | 0.0006 |
| FES Par | Amygdala vol | PDS ParYth | 6653 | 13 | Total | 0.0072 | -0.0394 | -0.0161 | 0.1753 | 0.0119 |
| FES Par | Amygdala vol | PDS Par | 6653 | 13 | Apath | -0.0227 | -0.0636 | -0.0431 | 0.0000 | 0.0104 |
| FES Par | Amygdala vol | PDS Par | 6653 | 13 | Bpath | 0.0037 | -0.0531 | -0.0247 | 0.0881 | 0.0145 |
| FES Par | Amygdala vol | PDS Par | 6653 | 13 | Direct | 0.0082 | -0.0384 | -0.0151 | 0.2045 | 0.0119 |
| FES Par | Amygdala vol | PDS Par | 6653 | 13 | Indirect | 0.0003 | -0.0024 | -0.0011 | 0.1144 | 0.0007 |
| FES Par | Amygdala vol | PDS Par | 6653 | 13 | Total | 0.0071 | -0.0394 | -0.0161 | 0.1740 | 0.0119 |
| FES Par | Amygdala vol | PDS Yth | 6653 | 13 | Apath | 0.0209 | -0.0317 | -0.0054 | 0.6886 | 0.0134 |
| FES Par | Amygdala vol | PDS Yth | 6653 | 13 | Bpath | 0.0199 | -0.0347 | -0.0074 | 0.5964 | 0.0139 |
| FES Par | Amygdala vol | PDS Yth | 6653 | 13 | Direct | 0.0075 | -0.0396 | -0.0161 | 0.1812 | 0.0120 |
| FES Par | Amygdala vol | PDS Yth | 6653 | 13 | Indirect | 0.0002 | -0.0003 | 0.0000 | 0.7481 | 0.0001 |
| FES Par | Amygdala vol | PDS Yth | 6653 | 13 | Total | 0.0074 | -0.0396 | -0.0161 | 0.1801 | 0.0120 |
| FES Par | L AmygCON | PDS ParYth | 6653 | 13 | Apath | -0.0063 | -0.0542 | -0.0303 | 0.0132 | 0.0122 |
| FES Par | L AmygCON | PDS ParYth | 6653 | 13 | Bpath | 0.0974 | 0.0351 | 0.0663 | 0.0000 | 0.0159 |
| FES Par | L AmygCON | PDS ParYth | 6653 | 13 | Direct | 0.0424 | -0.0051 | 0.0187 | 0.1229 | 0.0121 |
| FES Par | L AmygCON | PDS ParYth | 6653 | 13 | Indirect | 0.0038 | 0.0002 | 0.0020 | 0.0285 | 0.0009 |
| FES Par | L AmygCON | PDS ParYth | 6653 | 13 | Total | 0.0445 | -0.0031 | 0.0207 | 0.0878 | 0.0121 |
| FES Par | L AmygCON | PDS Par | 6653 | 13 | Apath | -0.0226 | -0.0640 | -0.0433 | 0.0000 | 0.0106 |
| FES Par | L AmygCON | PDS Par | 6653 | 13 | Bpath | 0.1194 | 0.0605 | 0.0899 | 0.0000 | 0.0150 |
| FES Par | L AmygCON | PDS Par | 6653 | 13 | Direct | 0.0408 | -0.0064 | 0.0172 | 0.1532 | 0.0120 |
| FES Par | L AmygCON | PDS Par | 6653 | 13 | Indirect | 0.0061 | 0.0017 | 0.0039 | 0.0006 | 0.0011 |
| FES Par | L AmygCON | PDS Par | 6653 | 13 | Total | 0.0447 | -0.0025 | 0.0211 | 0.0801 | 0.0120 |
| FES Par | L AmygCON | PDS Yth | 6653 | 13 | Apath | 0.0209 | -0.0310 | -0.0051 | 0.7019 | 0.0132 |
| FES Par | L AmygCON | PDS Yth | 6653 | 13 | Bpath | 0.0581 | 0.0005 | 0.0293 | 0.0465 | 0.0147 |
| FES Par | L AmygCON | PDS Yth | 6653 | 13 | Direct | 0.0441 | -0.0030 | 0.0205 | 0.0877 | 0.0120 |
| FES Par | L AmygCON | PDS Yth | 6653 | 13 | Indirect | 0.0009 | -0.0006 | 0.0001 | 0.7056 | 0.0004 |
| FES Par | L AmygCON | PDS Yth | 6653 | 13 | Total | 0.0443 | -0.0029 | 0.0207 | 0.0857 | 0.0120 |
| FES Par | R AmygCON | PDS ParYth | 6653 | 13 | Apath | -0.0061 | -0.0544 | -0.0303 | 0.0139 | 0.0123 |
| FES Par | R AmygCON | PDS ParYth | 6653 | 13 | Bpath | 0.0902 | 0.0281 | 0.0592 | 0.0002 | 0.0158 |
| FES Par | R AmygCON | PDS ParYth | 6653 | 13 | Direct | 0.0299 | -0.0187 | 0.0056 | 0.6495 | 0.0124 |
| FES Par | R AmygCON | PDS ParYth | 6653 | 13 | Indirect | 0.0035 | 0.0001 | 0.0018 | 0.0400 | 0.0009 |
| FES Par | R AmygCON | PDS ParYth | 6653 | 13 | Total | 0.0318 | -0.0169 | 0.0074 | 0.5498 | 0.0124 |
| FES Par | R AmygCON | PDS Par | 6653 | 13 | Apath | -0.0221 | -0.0639 | -0.0430 | 0.0001 | 0.0107 |
| FES Par | R AmygCON | PDS Par | 6653 | 13 | Bpath | 0.1084 | 0.0512 | 0.0798 | 0.0000 | 0.0146 |
| FES Par | R AmygCON | PDS Par | 6653 | 13 | Direct | 0.0285 | -0.0198 | 0.0043 | 0.7247 | 0.0123 |
| FES Par | R AmygCON | PDS Par | 6653 | 13 | Indirect | 0.0055 | 0.0014 | 0.0034 | 0.0010 | 0.0010 |
| FES Par | R AmygCON | PDS Par | 6653 | 13 | Total | 0.0319 | -0.0164 | 0.0078 | 0.5290 | 0.0123 |
| FES Par | R AmygCON | PDS Yth | 6653 | 13 | Apath | 0.0208 | -0.0311 | -0.0052 | 0.6967 | 0.0132 |
| FES Par | R AmygCON | PDS Yth | 6653 | 13 | Bpath | 0.0539 | -0.0033 | 0.0253 | 0.0830 | 0.0146 |
| FES Par | R AmygCON | PDS Yth | 6653 | 13 | Direct | 0.0313 | -0.0167 | 0.0073 | 0.5518 | 0.0123 |
| FES Par | R AmygCON | PDS Yth | 6653 | 13 | Indirect | 0.0008 | -0.0005 | 0.0001 | 0.7035 | 0.0003 |
| FES Par | R AmygCON | PDS Yth | 6653 | 13 | Total | 0.0315 | -0.0166 | 0.0074 | 0.5448 | 0.0123 |
| FES Yth | ACC CA | PDS ParYth | 6640 | 13 | Apath | -0.0534 | -0.1033 | -0.0784 | 0.0000 | 0.0127 |
| FES Yth | ACC CA | PDS ParYth | 6640 | 13 | Bpath | 0.0165 | -0.0429 | -0.0132 | 0.3840 | 0.0151 |
| FES Yth | ACC CA | PDS ParYth | 6640 | 13 | Direct | 0.0190 | -0.0306 | -0.0058 | 0.6454 | 0.0126 |
| FES Yth | ACC CA | PDS ParYth | 6640 | 13 | Indirect | 0.0013 | -0.0034 | -0.0010 | 0.3883 | 0.0012 |
| FES Yth | ACC CA | PDS ParYth | 6640 | 13 | Total | 0.0178 | -0.0315 | -0.0068 | 0.5865 | 0.0126 |
| FES Yth | ACC CA | PDS Par | 6640 | 13 | Apath | -0.0175 | -0.0590 | -0.0382 | 0.0003 | 0.0106 |
| FES Yth | ACC CA | PDS Par | 6640 | 13 | Bpath | -0.0088 | -0.0668 | -0.0378 | 0.0107 | 0.0148 |
| FES Yth | ACC CA | PDS Par | 6640 | 13 | Direct | 0.0186 | -0.0295 | -0.0054 | 0.6573 | 0.0123 |
| FES Yth | ACC CA | PDS Par | 6640 | 13 | Indirect | -0.0001 | -0.0028 | -0.0014 | 0.0345 | 0.0007 |
| FES Yth | ACC CA | PDS Par | 6640 | 13 | Total | 0.0171 | -0.0309 | -0.0069 | 0.5743 | 0.0123 |
| FES Yth | ACC CA | PDS Yth | 6640 | 13 | Apath | -0.0720 | -0.1252 | -0.0986 | 0.0000 | 0.0136 |
| FES Yth | ACC CA | PDS Yth | 6640 | 13 | Bpath | 0.0288 | -0.0255 | 0.0016 | 0.9069 | 0.0139 |
| FES Yth | ACC CA | PDS Yth | 6640 | 13 | Direct | 0.0174 | -0.0314 | -0.0070 | 0.5743 | 0.0125 |
| FES Yth | ACC CA | PDS Yth | 6640 | 13 | Indirect | 0.0028 | -0.0025 | 0.0002 | 0.9070 | 0.0014 |
| FES Yth | ACC CA | PDS Yth | 6640 | 13 | Total | 0.0175 | -0.0311 | -0.0068 | 0.5811 | 0.0124 |
| FES Yth | ACC CT | PDS ParYth | 6640 | 13 | Apath | -0.0539 | -0.1028 | -0.0783 | 0.0000 | 0.0125 |
| FES Yth | ACC CT | PDS ParYth | 6640 | 13 | Bpath | 0.0544 | -0.0079 | 0.0233 | 0.1431 | 0.0159 |
| FES Yth | ACC CT | PDS ParYth | 6640 | 13 | Direct | 0.0179 | -0.0321 | -0.0071 | 0.5759 | 0.0128 |
| FES Yth | ACC CT | PDS ParYth | 6640 | 13 | Indirect | 0.0043 | -0.0007 | 0.0018 | 0.1508 | 0.0013 |
| FES Yth | ACC CT | PDS ParYth | 6640 | 13 | Total | 0.0196 | -0.0302 | -0.0053 | 0.6762 | 0.0127 |
| FES Yth | ACC CT | PDS Par | 6640 | 13 | Apath | -0.0168 | -0.0593 | -0.0381 | 0.0004 | 0.0108 |
| FES Yth | ACC CT | PDS Par | 6640 | 13 | Bpath | 0.0744 | 0.0159 | 0.0452 | 0.0025 | 0.0149 |
| FES Yth | ACC CT | PDS Par | 6640 | 13 | Direct | 0.0177 | -0.0316 | -0.0070 | 0.5785 | 0.0126 |
| FES Yth | ACC CT | PDS Par | 6640 | 13 | Indirect | 0.0032 | 0.0003 | 0.0017 | 0.0203 | 0.0007 |
| FES Yth | ACC CT | PDS Par | 6640 | 13 | Total | 0.0194 | -0.0299 | -0.0053 | 0.6754 | 0.0126 |
| FES Yth | ACC CT | PDS Yth | 6640 | 13 | Apath | -0.0722 | -0.1251 | -0.0986 | 0.0000 | 0.0135 |
| FES Yth | ACC CT | PDS Yth | 6640 | 13 | Bpath | 0.0277 | -0.0280 | -0.0001 | 0.9924 | 0.0142 |
| FES Yth | ACC CT | PDS Yth | 6640 | 13 | Direct | 0.0195 | -0.0302 | -0.0053 | 0.6754 | 0.0127 |
| FES Yth | ACC CT | PDS Yth | 6640 | 13 | Indirect | 0.0027 | -0.0028 | 0.0000 | 0.9924 | 0.0014 |
| FES Yth | ACC CT | PDS Yth | 6640 | 13 | Total | 0.0194 | -0.0301 | -0.0053 | 0.6733 | 0.0126 |
| FES Yth | Amygdala vol | PDS ParYth | 6640 | 13 | Apath | -0.0539 | -0.1027 | -0.0783 | 0.0000 | 0.0124 |
| FES Yth | Amygdala vol | PDS ParYth | 6640 | 13 | Bpath | 0.0050 | -0.0568 | -0.0259 | 0.0998 | 0.0158 |
| FES Yth | Amygdala vol | PDS ParYth | 6640 | 13 | Direct | 0.0272 | -0.0203 | 0.0034 | 0.7762 | 0.0121 |
| FES Yth | Amygdala vol | PDS ParYth | 6640 | 13 | Indirect | 0.0005 | -0.0045 | -0.0020 | 0.1116 | 0.0013 |
| FES Yth | Amygdala vol | PDS ParYth | 6640 | 13 | Total | 0.0249 | -0.0221 | 0.0014 | 0.9063 | 0.0120 |
| FES Yth | Amygdala vol | PDS Par | 6640 | 13 | Apath | -0.0174 | -0.0590 | -0.0382 | 0.0003 | 0.0106 |
| FES Yth | Amygdala vol | PDS Par | 6640 | 13 | Bpath | 0.0039 | -0.0530 | -0.0245 | 0.0904 | 0.0145 |
| FES Yth | Amygdala vol | PDS Par | 6640 | 13 | Direct | 0.0261 | -0.0214 | 0.0023 | 0.8472 | 0.0121 |
| FES Yth | Amygdala vol | PDS Par | 6640 | 13 | Indirect | 0.0003 | -0.0021 | -0.0009 | 0.1222 | 0.0006 |
| FES Yth | Amygdala vol | PDS Par | 6640 | 13 | Total | 0.0251 | -0.0223 | 0.0014 | 0.9082 | 0.0121 |
| FES Yth | Amygdala vol | PDS Yth | 6640 | 13 | Apath | -0.0724 | -0.1249 | -0.0987 | 0.0000 | 0.0134 |
| FES Yth | Amygdala vol | PDS Yth | 6640 | 13 | Bpath | 0.0191 | -0.0368 | -0.0089 | 0.5342 | 0.0143 |
| FES Yth | Amygdala vol | PDS Yth | 6640 | 13 | Direct | 0.0262 | -0.0216 | 0.0023 | 0.8506 | 0.0122 |
| FES Yth | Amygdala vol | PDS Yth | 6640 | 13 | Indirect | 0.0019 | -0.0036 | -0.0009 | 0.5346 | 0.0014 |
| FES Yth | Amygdala vol | PDS Yth | 6640 | 13 | Total | 0.0251 | -0.0223 | 0.0014 | 0.9063 | 0.0121 |
| FES Yth | L AmygCON | PDS ParYth | 6640 | 13 | Apath | -0.0539 | -0.1033 | -0.0786 | 0.0000 | 0.0126 |
| FES Yth | L AmygCON | PDS ParYth | 6640 | 13 | Bpath | 0.0971 | 0.0330 | 0.0651 | 0.0001 | 0.0163 |
| FES Yth | L AmygCON | PDS ParYth | 6640 | 13 | Direct | 0.0637 | 0.0147 | 0.0392 | 0.0017 | 0.0125 |
| FES Yth | L AmygCON | PDS ParYth | 6640 | 13 | Indirect | 0.0081 | 0.0021 | 0.0051 | 0.0007 | 0.0015 |
| FES Yth | L AmygCON | PDS ParYth | 6640 | 13 | Total | 0.0686 | 0.0200 | 0.0443 | 0.0003 | 0.0124 |
| FES Yth | L AmygCON | PDS Par | 6640 | 13 | Apath | -0.0174 | -0.0591 | -0.0383 | 0.0003 | 0.0106 |
| FES Yth | L AmygCON | PDS Par | 6640 | 13 | Bpath | 0.1193 | 0.0594 | 0.0893 | 0.0000 | 0.0153 |
| FES Yth | L AmygCON | PDS Par | 6640 | 13 | Direct | 0.0657 | 0.0164 | 0.0411 | 0.0011 | 0.0126 |
| FES Yth | L AmygCON | PDS Par | 6640 | 13 | Indirect | 0.0056 | 0.0012 | 0.0034 | 0.0020 | 0.0011 |
| FES Yth | L AmygCON | PDS Par | 6640 | 13 | Total | 0.0692 | 0.0197 | 0.0445 | 0.0004 | 0.0126 |
| FES Yth | L AmygCON | PDS Yth | 6640 | 13 | Apath | -0.0723 | -0.1253 | -0.0988 | 0.0000 | 0.0135 |
| FES Yth | L AmygCON | PDS Yth | 6640 | 13 | Bpath | 0.0550 | -0.0031 | 0.0260 | 0.0795 | 0.0148 |
| FES Yth | L AmygCON | PDS Yth | 6640 | 13 | Direct | 0.0672 | 0.0158 | 0.0415 | 0.0016 | 0.0131 |
| FES Yth | L AmygCON | PDS Yth | 6640 | 13 | Indirect | 0.0055 | -0.0004 | 0.0026 | 0.0878 | 0.0015 |
| FES Yth | L AmygCON | PDS Yth | 6640 | 13 | Total | 0.0695 | 0.0187 | 0.0441 | 0.0007 | 0.0130 |
| FES Yth | R AmygCON | PDS ParYth | 6640 | 13 | Apath | -0.0542 | -0.1024 | -0.0783 | 0.0000 | 0.0123 |
| FES Yth | R AmygCON | PDS ParYth | 6640 | 13 | Bpath | 0.0874 | 0.0253 | 0.0563 | 0.0004 | 0.0159 |
| FES Yth | R AmygCON | PDS ParYth | 6640 | 13 | Direct | 0.0760 | 0.0222 | 0.0491 | 0.0003 | 0.0137 |
| FES Yth | R AmygCON | PDS ParYth | 6640 | 13 | Indirect | 0.0072 | 0.0016 | 0.0044 | 0.0018 | 0.0014 |
| FES Yth | R AmygCON | PDS ParYth | 6640 | 13 | Total | 0.0803 | 0.0267 | 0.0535 | 0.0001 | 0.0137 |
| FES Yth | R AmygCON | PDS Par | 6640 | 13 | Apath | -0.0173 | -0.0595 | -0.0384 | 0.0004 | 0.0108 |
| FES Yth | R AmygCON | PDS Par | 6640 | 13 | Bpath | 0.1072 | 0.0487 | 0.0779 | 0.0000 | 0.0149 |
| FES Yth | R AmygCON | PDS Par | 6640 | 13 | Direct | 0.0778 | 0.0235 | 0.0507 | 0.0003 | 0.0138 |
| FES Yth | R AmygCON | PDS Par | 6640 | 13 | Indirect | 0.0050 | 0.0010 | 0.0030 | 0.0034 | 0.0010 |
| FES Yth | R AmygCON | PDS Par | 6640 | 13 | Total | 0.0809 | 0.0265 | 0.0537 | 0.0001 | 0.0139 |
| FES Yth | R AmygCON | PDS Yth | 6640 | 13 | Apath | -0.0722 | -0.1252 | -0.0987 | 0.0000 | 0.0135 |
| FES Yth | R AmygCON | PDS Yth | 6640 | 13 | Bpath | 0.0510 | -0.0089 | 0.0211 | 0.1683 | 0.0153 |
| FES Yth | R AmygCON | PDS Yth | 6640 | 13 | Direct | 0.0783 | 0.0242 | 0.0512 | 0.0002 | 0.0138 |
| FES Yth | R AmygCON | PDS Yth | 6640 | 13 | Indirect | 0.0051 | -0.0009 | 0.0021 | 0.1762 | 0.0015 |
| FES Yth | R AmygCON | PDS Yth | 6640 | 13 | Total | 0.0800 | 0.0266 | 0.0533 | 0.0001 | 0.0136 |
| Par Fact | ACC CA | PDS ParYth | 6656 | 13 | Apath | -0.0239 | -0.0723 | -0.0481 | 0.0001 | 0.0123 |
| Par Fact | ACC CA | PDS ParYth | 6656 | 13 | Bpath | 0.0411 | -0.0177 | 0.0117 | 0.4351 | 0.0150 |
| Par Fact | ACC CA | PDS ParYth | 6656 | 13 | Direct | 0.0219 | -0.0270 | -0.0025 | 0.8389 | 0.0125 |
| Par Fact | ACC CA | PDS ParYth | 6656 | 13 | Indirect | 0.0009 | -0.0020 | -0.0006 | 0.4430 | 0.0007 |
| Par Fact | ACC CA | PDS ParYth | 6656 | 13 | Total | 0.0213 | -0.0275 | -0.0031 | 0.8034 | 0.0125 |
| Par Fact | ACC CA | PDS Par | 6656 | 13 | Apath | -0.0359 | -0.0767 | -0.0563 | 0.0000 | 0.0104 |
| Par Fact | ACC CA | PDS Par | 6656 | 13 | Bpath | 0.0664 | 0.0090 | 0.0377 | 0.0101 | 0.0146 |
| Par Fact | ACC CA | PDS Par | 6656 | 13 | Direct | 0.0237 | -0.0258 | -0.0010 | 0.9351 | 0.0126 |
| Par Fact | ACC CA | PDS Par | 6656 | 13 | Indirect | -0.0003 | -0.0039 | -0.0021 | 0.0204 | 0.0009 |
| Par Fact | ACC CA | PDS Par | 6656 | 13 | Total | 0.0215 | -0.0278 | -0.0031 | 0.8026 | 0.0126 |
| Par Fact | ACC CA | PDS Yth | 6656 | 13 | Apath | 0.0044 | -0.0481 | -0.0218 | 0.1031 | 0.0134 |
| Par Fact | ACC CA | PDS Yth | 6656 | 13 | Bpath | 0.0251 | -0.0294 | -0.0021 | 0.8776 | 0.0139 |
| Par Fact | ACC CA | PDS Yth | 6656 | 13 | Direct | 0.0213 | -0.0276 | -0.0031 | 0.8011 | 0.0125 |
| Par Fact | ACC CA | PDS Yth | 6656 | 13 | Indirect | 0.0006 | -0.0006 | 0.0000 | 0.8787 | 0.0003 |
| Par Fact | ACC CA | PDS Yth | 6656 | 13 | Total | 0.0214 | -0.0276 | -0.0031 | 0.8040 | 0.0125 |
| Par Fact | ACC CT | PDS ParYth | 6656 | 13 | Apath | -0.0238 | -0.0720 | -0.0479 | 0.0001 | 0.0123 |
| Par Fact | ACC CT | PDS ParYth | 6656 | 13 | Bpath | 0.0085 | -0.0530 | -0.0222 | 0.1559 | 0.0157 |
| Par Fact | ACC CT | PDS ParYth | 6656 | 13 | Direct | 0.0220 | -0.0274 | -0.0027 | 0.8321 | 0.0126 |
| Par Fact | ACC CT | PDS ParYth | 6656 | 13 | Indirect | 0.0026 | -0.0005 | 0.0011 | 0.1775 | 0.0008 |
| Par Fact | ACC CT | PDS ParYth | 6656 | 13 | Total | 0.0231 | -0.0263 | -0.0016 | 0.8985 | 0.0126 |
| Par Fact | ACC CT | PDS Par | 6656 | 13 | Apath | -0.0360 | -0.0766 | -0.0563 | 0.0000 | 0.0104 |
| Par Fact | ACC CT | PDS Par | 6656 | 13 | Bpath | -0.0173 | -0.0745 | -0.0459 | 0.0017 | 0.0146 |
| Par Fact | ACC CT | PDS Par | 6656 | 13 | Direct | 0.0200 | -0.0283 | -0.0041 | 0.7374 | 0.0123 |
| Par Fact | ACC CT | PDS Par | 6656 | 13 | Indirect | 0.0044 | 0.0007 | 0.0026 | 0.0063 | 0.0009 |
| Par Fact | ACC CT | PDS Par | 6656 | 13 | Total | 0.0226 | -0.0257 | -0.0015 | 0.8998 | 0.0123 |
| Par Fact | ACC CT | PDS Yth | 6656 | 13 | Apath | 0.0036 | -0.0473 | -0.0218 | 0.0927 | 0.0130 |
| Par Fact | ACC CT | PDS Yth | 6656 | 13 | Bpath | 0.0282 | -0.0261 | 0.0011 | 0.9392 | 0.0138 |
| Par Fact | ACC CT | PDS Yth | 6656 | 13 | Direct | 0.0227 | -0.0258 | -0.0016 | 0.8983 | 0.0124 |
| Par Fact | ACC CT | PDS Yth | 6656 | 13 | Indirect | 0.0006 | -0.0006 | 0.0000 | 0.9394 | 0.0003 |
| Par Fact | ACC CT | PDS Yth | 6656 | 13 | Total | 0.0226 | -0.0258 | -0.0016 | 0.8967 | 0.0124 |
| Par Fact | Amygdala vol | PDS ParYth | 6656 | 13 | Apath | -0.0248 | -0.0718 | -0.0483 | 0.0001 | 0.0120 |
| Par Fact | Amygdala vol | PDS ParYth | 6656 | 13 | Bpath | 0.0566 | -0.0055 | 0.0255 | 0.1069 | 0.0158 |
| Par Fact | Amygdala vol | PDS ParYth | 6656 | 13 | Direct | 0.0092 | -0.0379 | -0.0144 | 0.2308 | 0.0120 |
| Par Fact | Amygdala vol | PDS ParYth | 6656 | 13 | Indirect | 0.0004 | -0.0028 | -0.0012 | 0.1349 | 0.0008 |
| Par Fact | Amygdala vol | PDS ParYth | 6656 | 13 | Total | 0.0079 | -0.0391 | -0.0156 | 0.1924 | 0.0120 |
| Par Fact | Amygdala vol | PDS Par | 6656 | 13 | Apath | -0.0359 | -0.0768 | -0.0564 | 0.0000 | 0.0104 |
| Par Fact | Amygdala vol | PDS Par | 6656 | 13 | Bpath | 0.0534 | -0.0037 | 0.0248 | 0.0883 | 0.0146 |
| Par Fact | Amygdala vol | PDS Par | 6656 | 13 | Direct | 0.0092 | -0.0378 | -0.0143 | 0.2344 | 0.0120 |
| Par Fact | Amygdala vol | PDS Par | 6656 | 13 | Indirect | 0.0003 | -0.0031 | -0.0014 | 0.1021 | 0.0009 |
| Par Fact | Amygdala vol | PDS Par | 6656 | 13 | Total | 0.0078 | -0.0391 | -0.0157 | 0.1908 | 0.0120 |
| Par Fact | Amygdala vol | PDS Yth | 6656 | 13 | Apath | 0.0040 | -0.0476 | -0.0218 | 0.0970 | 0.0132 |
| Par Fact | Amygdala vol | PDS Yth | 6656 | 13 | Bpath | 0.0352 | -0.0191 | 0.0080 | 0.5621 | 0.0139 |
| Par Fact | Amygdala vol | PDS Yth | 6656 | 13 | Direct | 0.0078 | -0.0387 | -0.0155 | 0.1935 | 0.0119 |
| Par Fact | Amygdala vol | PDS Yth | 6656 | 13 | Indirect | 0.0004 | -0.0008 | -0.0002 | 0.5806 | 0.0003 |
| Par Fact | Amygdala vol | PDS Yth | 6656 | 13 | Total | 0.0077 | -0.0389 | -0.0156 | 0.1885 | 0.0119 |
| Par Fact | L AmygCON | PDS ParYth | 6656 | 13 | Apath | -0.0241 | -0.0717 | -0.0479 | 0.0001 | 0.0121 |
| Par Fact | L AmygCON | PDS ParYth | 6656 | 13 | Bpath | -0.0343 | -0.0974 | -0.0659 | 0.0000 | 0.0161 |
| Par Fact | L AmygCON | PDS ParYth | 6656 | 13 | Direct | 0.0480 | 0.0003 | 0.0241 | 0.0472 | 0.0122 |
| Par Fact | L AmygCON | PDS ParYth | 6656 | 13 | Indirect | 0.0053 | 0.0010 | 0.0032 | 0.0036 | 0.0011 |
| Par Fact | L AmygCON | PDS ParYth | 6656 | 13 | Total | 0.0511 | 0.0034 | 0.0273 | 0.0249 | 0.0122 |
| Par Fact | L AmygCON | PDS Par | 6656 | 13 | Apath | -0.0360 | -0.0772 | -0.0566 | 0.0000 | 0.0105 |
| Par Fact | L AmygCON | PDS Par | 6656 | 13 | Bpath | -0.0593 | -0.1195 | -0.0894 | 0.0000 | 0.0154 |
| Par Fact | L AmygCON | PDS Par | 6656 | 13 | Direct | 0.0463 | -0.0012 | 0.0225 | 0.0632 | 0.0121 |
| Par Fact | L AmygCON | PDS Par | 6656 | 13 | Indirect | 0.0076 | 0.0025 | 0.0051 | 0.0001 | 0.0013 |
| Par Fact | L AmygCON | PDS Par | 6656 | 13 | Total | 0.0514 | 0.0038 | 0.0276 | 0.0232 | 0.0122 |
| Par Fact | L AmygCON | PDS Yth | 6656 | 13 | Apath | 0.0040 | -0.0473 | -0.0216 | 0.0986 | 0.0131 |
| Par Fact | L AmygCON | PDS Yth | 6656 | 13 | Bpath | -0.0007 | -0.0574 | -0.0290 | 0.0450 | 0.0145 |
| Par Fact | L AmygCON | PDS Yth | 6656 | 13 | Direct | 0.0503 | 0.0031 | 0.0267 | 0.0266 | 0.0120 |
| Par Fact | L AmygCON | PDS Yth | 6656 | 13 | Indirect | 0.0016 | -0.0003 | 0.0006 | 0.2002 | 0.0005 |
| Par Fact | L AmygCON | PDS Yth | 6656 | 13 | Total | 0.0509 | 0.0037 | 0.0273 | 0.0232 | 0.0120 |
| Par Fact | R AmygCON | PDS ParYth | 6656 | 13 | Apath | -0.0237 | -0.0717 | -0.0477 | 0.0001 | 0.0122 |
| Par Fact | R AmygCON | PDS ParYth | 6656 | 13 | Bpath | -0.0282 | -0.0891 | -0.0586 | 0.0002 | 0.0155 |
| Par Fact | R AmygCON | PDS ParYth | 6656 | 13 | Direct | 0.0334 | -0.0136 | 0.0099 | 0.4100 | 0.0120 |
| Par Fact | R AmygCON | PDS ParYth | 6656 | 13 | Indirect | 0.0048 | 0.0008 | 0.0028 | 0.0052 | 0.0010 |
| Par Fact | R AmygCON | PDS ParYth | 6656 | 13 | Total | 0.0362 | -0.0108 | 0.0127 | 0.2903 | 0.0120 |
| Par Fact | R AmygCON | PDS Par | 6656 | 13 | Apath | -0.0355 | -0.0770 | -0.0563 | 0.0000 | 0.0106 |
| Par Fact | R AmygCON | PDS Par | 6656 | 13 | Bpath | -0.0509 | -0.1080 | -0.0794 | 0.0000 | 0.0146 |
| Par Fact | R AmygCON | PDS Par | 6656 | 13 | Direct | 0.0321 | -0.0151 | 0.0085 | 0.4813 | 0.0120 |
| Par Fact | R AmygCON | PDS Par | 6656 | 13 | Indirect | 0.0068 | 0.0022 | 0.0045 | 0.0001 | 0.0012 |
| Par Fact | R AmygCON | PDS Par | 6656 | 13 | Total | 0.0366 | -0.0107 | 0.0129 | 0.2828 | 0.0121 |
| Par Fact | R AmygCON | PDS Yth | 6656 | 13 | Apath | 0.0043 | -0.0475 | -0.0216 | 0.1021 | 0.0132 |
| Par Fact | R AmygCON | PDS Yth | 6656 | 13 | Bpath | 0.0037 | -0.0537 | -0.0250 | 0.0872 | 0.0146 |
| Par Fact | R AmygCON | PDS Yth | 6656 | 13 | Direct | 0.0362 | -0.0119 | 0.0122 | 0.3211 | 0.0123 |
| Par Fact | R AmygCON | PDS Yth | 6656 | 13 | Indirect | 0.0015 | -0.0004 | 0.0005 | 0.2436 | 0.0005 |
| Par Fact | R AmygCON | PDS Yth | 6656 | 13 | Total | 0.0367 | -0.0113 | 0.0127 | 0.2995 | 0.0123 |
| PMON | ACC CA | PDS ParYth | 6644 | 13 | Apath | -0.0524 | -0.1028 | -0.0776 | 0.0000 | 0.0128 |
| PMON | ACC CA | PDS ParYth | 6644 | 13 | Bpath | 0.0437 | -0.0160 | 0.0139 | 0.3634 | 0.0152 |
| PMON | ACC CA | PDS ParYth | 6644 | 13 | Direct | 0.0482 | -0.0005 | 0.0238 | 0.0552 | 0.0124 |
| PMON | ACC CA | PDS ParYth | 6644 | 13 | Indirect | 0.0013 | -0.0034 | -0.0011 | 0.3681 | 0.0012 |
| PMON | ACC CA | PDS ParYth | 6644 | 13 | Total | 0.0470 | -0.0015 | 0.0228 | 0.0659 | 0.0124 |
| PMON | ACC CA | PDS Par | 6644 | 13 | Apath | -0.0251 | -0.0684 | -0.0468 | 0.0000 | 0.0110 |
| PMON | ACC CA | PDS Par | 6644 | 13 | Bpath | 0.0681 | 0.0108 | 0.0395 | 0.0069 | 0.0146 |
| PMON | ACC CA | PDS Par | 6644 | 13 | Direct | 0.0490 | 0.0001 | 0.0246 | 0.0489 | 0.0125 |
| PMON | ACC CA | PDS Par | 6644 | 13 | Indirect | -0.0003 | -0.0034 | -0.0018 | 0.0232 | 0.0008 |
| PMON | ACC CA | PDS Par | 6644 | 13 | Total | 0.0471 | -0.0017 | 0.0227 | 0.0679 | 0.0124 |
| PMON | ACC CA | PDS Yth | 6644 | 13 | Apath | -0.0502 | -0.1050 | -0.0776 | 0.0000 | 0.0140 |
| PMON | ACC CA | PDS Yth | 6644 | 13 | Bpath | 0.0265 | -0.0288 | -0.0012 | 0.9345 | 0.0141 |
| PMON | ACC CA | PDS Yth | 6644 | 13 | Direct | 0.0475 | -0.0022 | 0.0226 | 0.0742 | 0.0127 |
| PMON | ACC CA | PDS Yth | 6644 | 13 | Indirect | 0.0022 | -0.0021 | 0.0001 | 0.9345 | 0.0011 |
| PMON | ACC CA | PDS Yth | 6644 | 13 | Total | 0.0475 | -0.0020 | 0.0227 | 0.0716 | 0.0126 |
| PMON | ACC CT | PDS ParYth | 6644 | 13 | Apath | -0.0523 | -0.1029 | -0.0776 | 0.0000 | 0.0129 |
| PMON | ACC CT | PDS ParYth | 6644 | 13 | Bpath | 0.0051 | -0.0575 | -0.0262 | 0.1005 | 0.0159 |
| PMON | ACC CT | PDS ParYth | 6644 | 13 | Direct | -0.0117 | -0.0624 | -0.0371 | 0.0041 | 0.0129 |
| PMON | ACC CT | PDS ParYth | 6644 | 13 | Indirect | 0.0045 | -0.0005 | 0.0020 | 0.1112 | 0.0013 |
| PMON | ACC CT | PDS ParYth | 6644 | 13 | Total | -0.0098 | -0.0603 | -0.0350 | 0.0065 | 0.0129 |
| PMON | ACC CT | PDS Par | 6644 | 13 | Apath | -0.0252 | -0.0685 | -0.0468 | 0.0000 | 0.0111 |
| PMON | ACC CT | PDS Par | 6644 | 13 | Bpath | -0.0194 | -0.0764 | -0.0479 | 0.0010 | 0.0145 |
| PMON | ACC CT | PDS Par | 6644 | 13 | Direct | -0.0124 | -0.0620 | -0.0372 | 0.0032 | 0.0126 |
| PMON | ACC CT | PDS Par | 6644 | 13 | Indirect | 0.0039 | 0.0006 | 0.0022 | 0.0078 | 0.0008 |
| PMON | ACC CT | PDS Par | 6644 | 13 | Total | -0.0102 | -0.0598 | -0.0350 | 0.0057 | 0.0126 |
| PMON | ACC CT | PDS Yth | 6644 | 13 | Apath | -0.0509 | -0.1043 | -0.0776 | 0.0000 | 0.0136 |
| PMON | ACC CT | PDS Yth | 6644 | 13 | Bpath | 0.0257 | -0.0282 | -0.0013 | 0.9268 | 0.0137 |
| PMON | ACC CT | PDS Yth | 6644 | 13 | Direct | -0.0101 | -0.0601 | -0.0351 | 0.0059 | 0.0128 |
| PMON | ACC CT | PDS Yth | 6644 | 13 | Indirect | 0.0022 | -0.0020 | 0.0001 | 0.9268 | 0.0011 |
| PMON | ACC CT | PDS Yth | 6644 | 13 | Total | -0.0100 | -0.0599 | -0.0350 | 0.0060 | 0.0127 |
| PMON | Amygdala vol | PDS ParYth | 6644 | 13 | Apath | -0.0530 | -0.1022 | -0.0776 | 0.0000 | 0.0126 |
| PMON | Amygdala vol | PDS ParYth | 6644 | 13 | Bpath | 0.0571 | -0.0041 | 0.0265 | 0.0893 | 0.0156 |
| PMON | Amygdala vol | PDS ParYth | 6644 | 13 | Direct | 0.0238 | -0.0251 | -0.0006 | 0.9613 | 0.0125 |
| PMON | Amygdala vol | PDS ParYth | 6644 | 13 | Indirect | 0.0004 | -0.0046 | -0.0021 | 0.1065 | 0.0013 |
| PMON | Amygdala vol | PDS ParYth | 6644 | 13 | Total | 0.0216 | -0.0269 | -0.0027 | 0.8297 | 0.0124 |
| PMON | Amygdala vol | PDS Par | 6644 | 13 | Apath | -0.0248 | -0.0687 | -0.0467 | 0.0000 | 0.0112 |
| PMON | Amygdala vol | PDS Par | 6644 | 13 | Bpath | 0.0529 | -0.0029 | 0.0250 | 0.0786 | 0.0142 |
| PMON | Amygdala vol | PDS Par | 6644 | 13 | Direct | 0.0227 | -0.0258 | -0.0015 | 0.9005 | 0.0124 |
| PMON | Amygdala vol | PDS Par | 6644 | 13 | Indirect | 0.0003 | -0.0026 | -0.0012 | 0.1068 | 0.0007 |
| PMON | Amygdala vol | PDS Par | 6644 | 13 | Total | 0.0214 | -0.0269 | -0.0027 | 0.8255 | 0.0123 |
| PMON | Amygdala vol | PDS Yth | 6644 | 13 | Apath | -0.0504 | -0.1049 | -0.0776 | 0.0000 | 0.0139 |
| PMON | Amygdala vol | PDS Yth | 6644 | 13 | Bpath | 0.0368 | -0.0178 | 0.0095 | 0.4967 | 0.0139 |
| PMON | Amygdala vol | PDS Yth | 6644 | 13 | Direct | 0.0224 | -0.0264 | -0.0020 | 0.8749 | 0.0124 |
| PMON | Amygdala vol | PDS Yth | 6644 | 13 | Indirect | 0.0014 | -0.0029 | -0.0007 | 0.5006 | 0.0011 |
| PMON | Amygdala vol | PDS Yth | 6644 | 13 | Total | 0.0216 | -0.0270 | -0.0027 | 0.8282 | 0.0124 |
| PMON | L AmygCON | PDS ParYth | 6644 | 13 | Apath | -0.0523 | -0.1031 | -0.0777 | 0.0000 | 0.0130 |
| PMON | L AmygCON | PDS ParYth | 6644 | 13 | Bpath | -0.0329 | -0.0961 | -0.0645 | 0.0001 | 0.0161 |
| PMON | L AmygCON | PDS ParYth | 6644 | 13 | Direct | 0.0599 | 0.0079 | 0.0339 | 0.0107 | 0.0133 |
| PMON | L AmygCON | PDS ParYth | 6644 | 13 | Indirect | 0.0080 | 0.0021 | 0.0050 | 0.0008 | 0.0015 |
| PMON | L AmygCON | PDS ParYth | 6644 | 13 | Total | 0.0646 | 0.0131 | 0.0389 | 0.0031 | 0.0131 |
| PMON | L AmygCON | PDS Par | 6644 | 13 | Apath | -0.0248 | -0.0689 | -0.0469 | 0.0000 | 0.0113 |
| PMON | L AmygCON | PDS Par | 6644 | 13 | Bpath | -0.0586 | -0.1192 | -0.0889 | 0.0000 | 0.0154 |
| PMON | L AmygCON | PDS Par | 6644 | 13 | Direct | 0.0611 | 0.0092 | 0.0352 | 0.0079 | 0.0132 |
| PMON | L AmygCON | PDS Par | 6644 | 13 | Indirect | 0.0066 | 0.0018 | 0.0042 | 0.0007 | 0.0012 |
| PMON | L AmygCON | PDS Par | 6644 | 13 | Total | 0.0653 | 0.0134 | 0.0393 | 0.0030 | 0.0132 |
| PMON | L AmygCON | PDS Yth | 6644 | 13 | Apath | -0.0505 | -0.1046 | -0.0775 | 0.0000 | 0.0138 |
| PMON | L AmygCON | PDS Yth | 6644 | 13 | Bpath | 0.0029 | -0.0550 | -0.0261 | 0.0780 | 0.0148 |
| PMON | L AmygCON | PDS Yth | 6644 | 13 | Direct | 0.0636 | 0.0098 | 0.0367 | 0.0075 | 0.0137 |
| PMON | L AmygCON | PDS Yth | 6644 | 13 | Indirect | 0.0044 | -0.0003 | 0.0020 | 0.0945 | 0.0012 |
| PMON | L AmygCON | PDS Yth | 6644 | 13 | Total | 0.0653 | 0.0122 | 0.0388 | 0.0043 | 0.0136 |
| PMON | R AmygCON | PDS ParYth | 6644 | 13 | Apath | -0.0528 | -0.1031 | -0.0779 | 0.0000 | 0.0128 |
| PMON | R AmygCON | PDS ParYth | 6644 | 13 | Bpath | -0.0281 | -0.0901 | -0.0591 | 0.0002 | 0.0158 |
| PMON | R AmygCON | PDS ParYth | 6644 | 13 | Direct | 0.0408 | -0.0125 | 0.0142 | 0.2978 | 0.0136 |
| PMON | R AmygCON | PDS ParYth | 6644 | 13 | Indirect | 0.0075 | 0.0017 | 0.0046 | 0.0019 | 0.0015 |
| PMON | R AmygCON | PDS ParYth | 6644 | 13 | Total | 0.0452 | -0.0077 | 0.0188 | 0.1648 | 0.0135 |
| PMON | R AmygCON | PDS Par | 6644 | 13 | Apath | -0.0251 | -0.0687 | -0.0469 | 0.0000 | 0.0111 |
| PMON | R AmygCON | PDS Par | 6644 | 13 | Bpath | -0.0501 | -0.1072 | -0.0786 | 0.0000 | 0.0146 |
| PMON | R AmygCON | PDS Par | 6644 | 13 | Direct | 0.0416 | -0.0107 | 0.0155 | 0.2459 | 0.0133 |
| PMON | R AmygCON | PDS Par | 6644 | 13 | Indirect | 0.0059 | 0.0015 | 0.0037 | 0.0011 | 0.0011 |
| PMON | R AmygCON | PDS Par | 6644 | 13 | Total | 0.0453 | -0.0070 | 0.0192 | 0.1507 | 0.0133 |
| PMON | R AmygCON | PDS Yth | 6644 | 13 | Apath | -0.0512 | -0.1042 | -0.0777 | 0.0000 | 0.0135 |
| PMON | R AmygCON | PDS Yth | 6644 | 13 | Bpath | 0.0043 | -0.0532 | -0.0245 | 0.0953 | 0.0147 |
| PMON | R AmygCON | PDS Yth | 6644 | 13 | Direct | 0.0427 | -0.0092 | 0.0167 | 0.2059 | 0.0132 |
| PMON | R AmygCON | PDS Yth | 6644 | 13 | Indirect | 0.0042 | -0.0004 | 0.0019 | 0.1100 | 0.0012 |
| PMON | R AmygCON | PDS Yth | 6644 | 13 | Total | 0.0445 | -0.0072 | 0.0187 | 0.1576 | 0.0132 |
As we see, the table length for a single path type, such as Direct effect, is 135.
For these plots, as is used in prior work, we report the ordered effects across out multiverse of indirect, direct and total effects. There are some additional inferential procedures we can consider, see Dani Cosme’s Documentation example. However, these bootstrapped estimates are typically done for simple X~Y models and would require tailoring for the mediation. Some other representations for specr models:
Of note, while there are no significant effects here (conf.high/conf/low overlap with 0), these plots will color points blue/red for p < .05 for post/neg effects.
How to interpret the specification curve plots? The
multi-panel figures represent the effect size and significance in
Panel A and the variables (significance denoted blue/red
and null denoted gray) used in the models in Panel B. The
interpretation of these panels are straightforward, the significant
negative effect (red) in panel B, will have an associated X (predictor),
M (mediator) and Y (outcome) denoted in red that was fit for that model.
All of these models also controlled for age, sex, and race.
Like in Rinjhart 2021, we create a specification curve figures for the (1) Direct effect, (2) Indirect effect and (3) Total effect. For each figure, the effects are ordered, from least to greatist, thus, the order of variables/effects across these three figures will not always overlap.
specr_direct <- mediation_multiverse %>%
filter(Effect == "Direct")
specr_direct$controls <- "Age + Race + Sex"
plot_a <- plot_curve(df = specr_direct,
ci = TRUE, desc = FALSE, legend = FALSE, null = 0) +
labs(caption = "Blue = Significant Positive (p < .05); Red = Significant Negative (p < .05)")
plot_b <- plot_choices(df = specr_direct, choices = c("X", "Y", "M"), desc = F, null = 0) +
labs(y = "Variables", x = "Ordered Specification Curve \n Direct Effect")
#jpeg("Figures/Aim2/DirectEffect_Multiverse.jpeg", units = "in",
# width = 10, height = 5, res = 300)
cowplot::plot_grid(plot_a, plot_b, ncol = 1, align = "v", axis = 'tblr',
labels = c('A', 'B'), rel_heights = c(1, 2),
label_fontfamily = "Times", label_size = 12)#dev.off()specr_indirect <- mediation_multiverse %>%
filter(Effect == "Indirect")
specr_indirect$controls <- "Age + Race + Sex"
plot_a_ind <- plot_curve(df = specr_indirect,
ci = TRUE, desc = F, legend = F) +
labs(caption = "Blue = Significant Positive (p < .05); Red = Significant Negative (p < .05)")
plot_b_ind <- plot_choices(df = specr_indirect, choices = c("X", "Y", "M"), desc = F) +
labs(y = "Variables", x = "Ordered Specification Curve \n Indirect Effect")
#jpeg("Figures/Aim2/IndirectEffect_Multiverse.jpeg", units = "in",
# width = 10, height = 5, res = 300)
cowplot::plot_grid(plot_a_ind, plot_b_ind, ncol = 1, align = "v", axis = 'tblr',
labels = c('A', 'B'), rel_heights = c(1, 2),
label_fontfamily = "Times", label_size = 12)#dev.off()specr_total <- mediation_multiverse %>%
filter(Effect == "Total")
specr_total$controls <- "Age + Race + Sex"
plot_a_tot <- plot_curve(df = specr_total,
ci = TRUE, desc = F, legend = T) +
labs(caption = "Blue = Significant Positive (p < .05); Red = Significant Negative (p < .05)")
plot_b_tot <- plot_choices(df = specr_total, choices = c("X", "Y", "M"), desc = F) +
labs(y = "Variables", x = "Ordered Specification Curve \n Total Effect")
#jpeg("Figures/Aim2/TotalEffect_Multiverse.jpeg", units = "in",
# width = 10, height = 5, res = 300)
cowplot::plot_grid(plot_a_tot, plot_b_tot, ncol = 1, align = "v", axis = 'tblr',
labels = c('A', 'B'), rel_heights = c(1, 2),
label_fontfamily = "Times", label_size = 12)#dev.off()specr_apth <- mediation_multiverse %>%
filter(Effect == "Apath")
specr_apth$controls <- "Age + Race + Sex"
plot_a <- plot_curve(df = specr_apth,
ci = TRUE, desc = F, legend = F) +
labs(caption = "Blue = Significant Positive (p < .05); Red = Significant Negative (p < .05)")
plot_b <- plot_choices(df = specr_apth, choices = c("X", "Y", "M"), desc = F) +
labs(y = "Variables", x = "Ordered Specification Curve \n A-path Effect")
#jpeg("Figures/Aim2/Apath_Multiverse.jpeg", units = "in",
# width = 10, height = 5, res = 300)
cowplot::plot_grid(plot_a, plot_b, ncol = 1, align = "v", axis = 'tblr',
labels = c('A', 'B'), rel_heights = c(1, 2),
label_fontfamily = "Times", label_size = 12)#dev.off()specr_bpth <- mediation_multiverse %>%
filter(Effect == "Bpath")
specr_bpth$controls <- "Age + Race + Sex"
plot_a <- plot_curve(df = specr_bpth,
ci = TRUE, desc = F, legend = F) +
labs(caption = "Blue = Significant Positive (p < .05); Red = Significant Negative (p < .05)")
plot_b <- plot_choices(df = specr_bpth, choices = c("X", "Y", "M"), desc = F) +
labs(y = "Variables", x = "Ordered Specification Curve \n B-path Effect")
#jpeg("Figures/Aim2/Bpath_Multiverse.jpeg", units = "in",
# width = 10, height = 5, res = 300)
cowplot::plot_grid(plot_a, plot_b, ncol = 1, align = "v", axis = 'tblr',
labels = c('A', 'B'), rel_heights = c(1, 2),
label_fontfamily = "Times", label_size = 12)#dev.off()mediation_multiverse %>%
dplyr::group_by(Effect, M) %>%
mutate(N_Effect = n()) %>%
filter(p.value <= .05) %>%
dplyr::summarize(count_sig = n()) %>%
group_by(Effect, M) %>%
dplyr::summarize(perc_sig = round((count_sig/45),2), count_sig)## # A tibble: 15 × 4
## # Groups: Effect [5]
## Effect M perc_sig count_sig
## <chr> <chr> <dbl> <int>
## 1 Apath PDS Par 1 45
## 2 Apath PDS ParYth 1 45
## 3 Apath PDS Yth 0.78 35
## 4 Bpath PDS Par 0.78 35
## 5 Bpath PDS ParYth 0.38 17
## 6 Bpath PDS Yth 0.04 2
## 7 Direct PDS Par 0.42 19
## 8 Direct PDS ParYth 0.42 19
## 9 Direct PDS Yth 0.44 20
## 10 Indirect PDS Par 0.78 35
## 11 Indirect PDS ParYth 0.38 17
## 12 Indirect PDS Yth 0.04 2
## 13 Total PDS Par 0.44 20
## 14 Total PDS ParYth 0.44 20
## 15 Total PDS Yth 0.44 20
Here we provide an example of how estimates can vary across factor derived scores across the 5 DVs for the parent reported PDS scores. Below, we plot the beta estimate + 95% CI for family factor (filled square) for each of the 5 DVs, In addition to this, we include the beta estimates for each o the 5 DVs for the parent reported PDS and the following factors:
These helps visualize how much a variable can vary in it’s indirect & direct effect w/ between the IV (environmental factors) and DVs (brain outcomes)
color_1 <- cbPalette <- c("black", "chocolate1", "firebrick", "slateblue", "turquoise4")# six colours for six factors
#jpeg("Figures/Aim2/WithinFactor_betas.jpeg", units = "in",
# width = 10, height = 6, res = 250)
mediation_multiverse %>%
ggplot(aes(colour = Y)) +
geom_errorbar(aes(x = Y, y = estimate,
ymin = conf.low, ymax = conf.high),
width = .1, alpha = .5,
data = . %>% filter(X %in% "FamEnv Fact", M %in% "PDS Par")) +
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 15, # filled square
data = . %>% filter(X %in% "FamEnv Fact", M %in% "PDS Par")) + # subset data for factor + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 16, # circle
position = position_jitterdodge(),
data = . %>% filter(X %in% "Par Fact", M %in% "PDS Par")) + # subset data for factor + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 17, # triangle
position = position_jitterdodge(),
data = . %>% filter(X %in% "Demo Fact", M %in% "PDS Par")) + # subset data for factor + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 18, # X
position = position_jitterdodge(),
data = . %>% filter(X %in% "Child Fact", M %in% "PDS Par")) + # subset data for factor + par puberty
labs(y = "Beta Estimates",
colour = "Brain Outcome") +
scale_colour_manual(values = color_1)+
facet_wrap(~Effect, scale = "free")+
theme_minimal()+
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(),
axis.line.x = element_blank(), axis.text.x = element_blank())#dev.off()Here we provide an example of how estimates can vary across individual measures across the 5 DVs for the parent reported PDS scores. Below, we plot the beta estimate + 95% CI for family factor (filled square) for each of the 5 DVs, In addition to this, we include the beta estimates for each of the 5 DVs for the parent reported PDS and the following measures:
“FES_youth”,“FES_parent”, “PMON”,“CRPBI”,“Avg_IncomeEduc”
These helps visualize how much a variable can vary in it’s indirect & direct effect w/ between the IV (environmental factors) and DVs (brain outcomes)
color_1 <- cbPalette <- c("black", "chocolate1", "firebrick", "slateblue", "turquoise4")# six colours for six factors
#jpeg("Figures/Aim2/WithinMeasure_betas.jpeg", units = "in",
# width = 10, height = 6, res = 250)
mediation_multiverse %>%
ggplot(aes(colour = Y)) +
geom_errorbar(aes(x = Y, y = estimate,
ymin = conf.low, ymax = conf.high),
width = .1, alpha = .5,
data = . %>% filter(X %in% "FamEnv Fact", M %in% "PDS Par")) +
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 15, # filled square
data = . %>% filter(X %in% "FamEnv Fact", M %in% "PDS Par")) + # subset data for factor + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 1, # circle
position = position_jitterdodge(),
data = . %>% filter(X %in% "FES Yth", M %in% "PDS Par")) + # subset data for FES youth + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 3, # +
position = position_jitterdodge(),
data = . %>% filter(X %in% "FES Par", M %in% "PDS Par")) + # subset data for FES parent + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 4, # x
position = position_jitterdodge(),
data = . %>% filter(X %in% "PMON", M %in% "PDS Par")) + # subset data for Parental monitoring + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 5, # diamond
position = position_jitterdodge(),
data = . %>% filter(X %in% "Par Accept", M %in% "PDS Par")) + # subset data for Parental acceptance + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 8, # star
position = position_jitterdodge(),
data = . %>% filter(X %in% "Avg IncEdu", M %in% "PDS Par")) + # subset data for Avg Inc/Edu + par puberty
labs(y = "Beta Estimates",
colour = "Brain Outcome") +
scale_colour_manual(values = color_1)+
facet_wrap(~Effect, scale = "free")+
theme_minimal()+
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(),
axis.line.x = element_blank(), axis.text.x = element_blank())#dev.off()Reduced to only indirect & direct effects
IndDir_A <- mediation_multiverse %>%
filter(Effect == "Direct" | Effect == "Indirect") %>%
ggplot(aes(colour = Y)) +
geom_errorbar(aes(x = Y, y = estimate,
ymin = conf.low, ymax = conf.high),
width = .1, alpha = .5,
data = . %>% filter(X %in% "FamEnv Fact", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) +
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 15, # filled square
data = . %>% filter(X %in% "FamEnv Fact", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for factor + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 16, # circle
position = position_jitterdodge(),
data = . %>% filter(X %in% "Par Fact", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for factor + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 17, # triangle
position = position_jitterdodge(),
data = . %>% filter(X %in% "Demo Fact", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for factor + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 18, # diamond
position = position_jitterdodge(),
data = . %>% filter(X %in% "Child Fact", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for factor + par puberty
labs(y = "Beta Estimates",
colour = "Brain Outcome"
#caption = "Square = FamEnv Factor; Circle: Parent Factor \n Triangle = Demographic Factor; X = Child Factor"
) +
scale_colour_manual(values = color_1)+
facet_wrap(~Effect, scale = "free")+
theme_minimal()+
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(),
axis.line.x = element_blank(), axis.text.x = element_blank())
IndDir_B <- mediation_multiverse %>%
filter(Effect == "Direct" | Effect == "Indirect") %>%
ggplot(aes(colour = Y)) +
geom_errorbar(aes(x = Y, y = estimate,
ymin = conf.low, ymax = conf.high),
width = .1, alpha = .5,
data = . %>% filter(X %in% "FamEnv Fact", M %in% "PDS Par")) +
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 15, # filled square
data = . %>% filter(X %in% "FamEnv Fact", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for factor + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 1, # circle
position = position_jitterdodge(),
data = . %>% filter(X %in% "FES Yth", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for FES youth + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 3, # +
position = position_jitterdodge(),
data = . %>% filter(X %in% "FES Par", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for FES parent + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 4, # x
position = position_jitterdodge(),
data = . %>% filter(X %in% "PMON", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for Parental monitoring + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 5, # diamond
position = position_jitterdodge(),
data = . %>% filter(X %in% "Par Accept", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for Parental acceptance + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 8, # star
position = position_jitterdodge(),
data = . %>% filter(X %in% "Avg IncEdu", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for Avg Inc/Edu + par puberty
labs(y = "Beta Estimates",
colour = "Brain Outcome"
#caption = "Square = FamEnv Factor; Circle = FES Yth; Plus = FES; \n X = PMON; Diamond = CRPBI; Asterisk = Avg Inc/Edu"
) +
scale_colour_manual(values = color_1)+
facet_wrap(~Effect, scale = "free")+
theme_minimal()+
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(),
axis.line.x = element_blank(), axis.text.x = element_blank(),
legend.position = "none")
#jpeg("Figures/Aim2/WithinFactMeasure_DirInd.jpeg", units = "in",
# width = 9, height = 5, res = 300)
#IndDir_A / IndDir_B + plot_layout(guides = 'collect')
cowplot::plot_grid(IndDir_A, IndDir_B, ncol = 1, align = "v", axis = 'tblr',
labels = c('A', 'B'), rel_heights = c(1, 1),
label_fontfamily = "Times", label_size = 14)#dev.off()Reduced to rsfmri to visual differences in closely clustered data that are scaled diff in structural estimates
color_2 <- cbPalette <- c("slateblue", "turquoise4")
IndDir_A_rsf <- mediation_multiverse %>%
filter(Y == "R AmygCON" | Y == "L AmygCON") %>%
filter(Effect == "Direct" | Effect == "Indirect") %>%
ggplot(aes(colour = Y)) +
geom_errorbar(aes(x = Y, y = estimate,
ymin = conf.low, ymax = conf.high),
width = .1, alpha = .5,
data = . %>% filter(X %in% "FamEnv Fact", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) +
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 15, # filled square
data = . %>% filter(X %in% "FamEnv Fact", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for factor + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 16, # circle
position = position_jitterdodge(),
data = . %>% filter(X %in% "Par Fact", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for factor + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 17, # triangle
position = position_jitterdodge(),
data = . %>% filter(X %in% "Demo Fact", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for factor + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 18, # diamond
position = position_jitterdodge(),
data = . %>% filter(X %in% "Child Fact", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for factor + par puberty
labs(y = "Beta Estimates",
colour = "Brain Outcome"
#caption = "Square = FamEnv Factor; Circle: Parent Factor \n Triangle = Demographic Factor; X = Child Factor"
) +
scale_colour_manual(values = color_2)+
facet_wrap(~Effect, scale = "free")+
theme_minimal()+
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(),
axis.line.x = element_blank(), axis.text.x = element_blank())
IndDir_B_rsf <- mediation_multiverse %>%
filter(Y == "R AmygCON" | Y == "L AmygCON") %>%
filter(Effect == "Direct" | Effect == "Indirect") %>%
ggplot(aes(colour = Y)) +
geom_errorbar(aes(x = Y, y = estimate,
ymin = conf.low, ymax = conf.high),
width = .1, alpha = .5,
data = . %>% filter(X %in% "FamEnv Fact", M %in% "PDS Par")) +
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 15, # filled square
data = . %>% filter(X %in% "FamEnv Fact", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for factor + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 1, # circle
position = position_jitterdodge(),
data = . %>% filter(X %in% "FES Yth", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for FES youth + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 3, # +
position = position_jitterdodge(),
data = . %>% filter(X %in% "FES Par", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for FES parent + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 4, # x
position = position_jitterdodge(),
data = . %>% filter(X %in% "PMON", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for Parental monitoring + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 5, # diamond
position = position_jitterdodge(),
data = . %>% filter(X %in% "Par Accept", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for Parental acceptance + par puberty
geom_point(aes(x = Y, y = estimate), fill = "white", shape = 8, # star
position = position_jitterdodge(),
data = . %>% filter(X %in% "Avg IncEdu", M %in% "PDS Par",
Effect %in% c("Direct", "Indirect"))) + # subset data for Avg Inc/Edu + par puberty
labs(y = "Beta Estimates",
colour = "Brain Outcome"
#caption = "Square = FamEnv Factor; Circle = FES Yth; Plus = FES; \n X = PMON; Diamond = CRPBI; Asterisk = Avg Inc/Edu"
) +
scale_colour_manual(values = color_2)+
facet_wrap(~Effect, scale = "free")+
theme_minimal()+
theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(),
axis.line.x = element_blank(), axis.text.x = element_blank(),
legend.position = "none")
#jpeg("Figures/Aim2/WithinFactMeasure_rfMRIonly_DirInd.jpeg", units = "in",
# width = 9, height = 5, res = 300)
cowplot::plot_grid(IndDir_A_rsf, IndDir_B_rsf, ncol = 1, align = "v", axis = 'tblr',
labels = c('A', 'B'), rel_heights = c(1, 1),
label_fontfamily = "Times", label_size = 14)#dev.off()To help with the interpretability/overlap of the effects in the above, we provide the pearson product correlations below. Some values may be opposite direction of others, such as Demographics and Family environment factors.
We summarize these correlations in a table and a shaded plot below. Some information about how to use the plotting function, see the corrplot information. The plot includes significance values for each correlation:
var_list = data.frame(
# Covariates
"Age" = data$Age, "Sex" = data$sex_r,
# IVS
"FamEnv_Fact" = data$FamEnv_Fact,
"Demo_Fact" = data$Demo_Fact,
"Child_Fact" = data$Child_Fact,
"Par_Fact" = data$Par_Fact,
"FES_Yth" = data$FES_youth,"FES_Par" = data$FES_parent,
"PMON" = data$PMON,"Par_Accept" = data$CRPBI, "Avg_IncEdu" = data$Avg_IncomeEduc,
# Mediators
"Par_PDS" = data$p_puberty,"Yth_PDS" = data$y_puberty,"ParYth_PDS" = data$avg_puberty,
# Dependent variables
"Amyg_Vol" = data$Amygdala_vol,
"ACC_CT" = data$ACC_CT,"ACC_CA" = data$ACC_CA,
"L_AmygCON" = data$L_AmygCing_rest, "R_AmygCON" = data$R_AmygCing_rest
)
#Var_corr = cor(var_list, use = "pairwise", method = "pearson")
Var_corr = rcorr(as.matrix(var_list), type = "pearson")
# rename column/row names for r-values to updated printed table
colnames(Var_corr$r) <- c("1","2","3","4","5","6","7","8","9","10","11","12",
"13","14","15","16","17","18","19")
rownames(Var_corr$r) <- c("1. Age", "2. Sex", "3. FamEnv Fact",
"4. Demo Fact", "5. Child Fact", "6, Par Fact",
"7. FES Yth", "8. FES Par", "9. PMON", "10. Par Accept",
"11. Avg IncEdu", "12. PDS Par", "13. PDS Yth",
"14. PDS ParYth", "15. Amyg Vol", "17. ACC CT",
"17. ACC CA", "18. L AmygCON", "19. R AmygCON")
knitr::kable(Var_corr$r, digits = 2) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
full_width = F, html_font = "Times") | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
1.00 | 0.03 | 0.04 | 0.01 | 0.07 | -0.01 | 0.06 | -0.01 | 0.10 | 0.01 | 0.01 | 0.18 | 0.12 | 0.18 | 0.08 | -0.17 | 0.14 | 0.00 | 0.00 |
|
0.03 | 1.00 | -0.08 | 0.01 | -0.15 | -0.04 | -0.06 | -0.03 | -0.17 | -0.05 | 0.01 | -0.48 | -0.19 | -0.42 | 0.14 | -0.06 | 0.04 | 0.03 | -0.01 |
|
0.04 | -0.08 | 1.00 | 0.64 | 0.59 | 0.70 | 0.54 | 0.64 | 0.41 | 0.40 | 0.59 | -0.13 | -0.13 | -0.14 | -0.02 | -0.01 | -0.01 | 0.11 | 0.09 |
|
0.01 | 0.01 | 0.64 | 1.00 | 0.13 | 0.13 | 0.15 | 0.09 | 0.15 | 0.06 | 0.95 | -0.24 | -0.17 | -0.23 | 0.00 | 0.01 | -0.04 | 0.16 | 0.12 |
|
0.07 | -0.15 | 0.59 | 0.13 | 1.00 | 0.13 | 0.57 | 0.12 | 0.70 | 0.81 | 0.12 | 0.04 | -0.07 | -0.01 | -0.01 | -0.03 | 0.04 | 0.04 | 0.04 |
| 6, Par Fact | -0.01 | -0.04 | 0.70 | 0.13 | 0.13 | 1.00 | 0.22 | 0.94 | 0.04 | 0.08 | 0.11 | -0.04 | -0.02 | -0.03 | -0.02 | 0.00 | -0.01 | 0.03 | 0.01 |
|
0.06 | -0.06 | 0.54 | 0.15 | 0.57 | 0.22 | 1.00 | 0.20 | 0.24 | 0.29 | 0.14 | 0.00 | -0.08 | -0.04 | 0.00 | -0.01 | 0.00 | 0.04 | 0.05 |
|
-0.01 | -0.03 | 0.64 | 0.09 | 0.12 | 0.94 | 0.20 | 1.00 | 0.05 | 0.07 | 0.07 | -0.03 | 0.00 | -0.02 | -0.02 | 0.00 | 0.00 | 0.02 | 0.01 |
|
0.10 | -0.17 | 0.41 | 0.15 | 0.70 | 0.04 | 0.24 | 0.05 | 1.00 | 0.36 | 0.14 | 0.06 | -0.03 | 0.03 | -0.02 | -0.04 | 0.03 | 0.03 | 0.02 |
|
0.01 | -0.05 | 0.40 | 0.06 | 0.81 | 0.08 | 0.29 | 0.07 | 0.36 | 1.00 | 0.05 | -0.02 | -0.05 | -0.04 | 0.00 | -0.01 | 0.04 | 0.03 | 0.03 |
|
0.01 | 0.01 | 0.59 | 0.95 | 0.12 | 0.11 | 0.14 | 0.07 | 0.14 | 0.05 | 1.00 | -0.19 | -0.15 | -0.19 | 0.00 | 0.03 | -0.04 | 0.14 | 0.12 |
|
0.18 | -0.48 | -0.13 | -0.24 | 0.04 | -0.04 | 0.00 | -0.03 | 0.06 | -0.02 | -0.19 | 1.00 | 0.43 | 0.85 | -0.04 | -0.04 | 0.04 | -0.09 | -0.06 |
|
0.12 | -0.19 | -0.13 | -0.17 | -0.07 | -0.02 | -0.08 | 0.00 | -0.03 | -0.05 | -0.15 | 0.43 | 1.00 | 0.84 | -0.01 | -0.01 | 0.01 | -0.04 | -0.03 |
|
0.18 | -0.42 | -0.14 | -0.23 | -0.01 | -0.03 | -0.04 | -0.02 | 0.03 | -0.04 | -0.19 | 0.85 | 0.84 | 1.00 | -0.03 | -0.02 | 0.02 | -0.08 | -0.06 |
|
0.08 | 0.14 | -0.02 | 0.00 | -0.01 | -0.02 | 0.00 | -0.02 | -0.02 | 0.00 | 0.00 | -0.04 | -0.01 | -0.03 | 1.00 | -0.04 | 0.08 | 0.01 | -0.02 |
|
-0.17 | -0.06 | -0.01 | 0.01 | -0.03 | 0.00 | -0.01 | 0.00 | -0.04 | -0.01 | 0.03 | -0.04 | -0.01 | -0.02 | -0.04 | 1.00 | -0.44 | -0.01 | 0.02 |
|
0.14 | 0.04 | -0.01 | -0.04 | 0.04 | -0.01 | 0.00 | 0.00 | 0.03 | 0.04 | -0.04 | 0.04 | 0.01 | 0.02 | 0.08 | -0.44 | 1.00 | -0.02 | -0.03 |
|
0.00 | 0.03 | 0.11 | 0.16 | 0.04 | 0.03 | 0.04 | 0.02 | 0.03 | 0.03 | 0.14 | -0.09 | -0.04 | -0.08 | 0.01 | -0.01 | -0.02 | 1.00 | 0.57 |
|
0.00 | -0.01 | 0.09 | 0.12 | 0.04 | 0.01 | 0.05 | 0.01 | 0.02 | 0.03 | 0.12 | -0.06 | -0.03 | -0.06 | -0.02 | 0.02 | -0.03 | 0.57 | 1.00 |
#jpeg("Figures/Descriptives/Correlations_Vars.jpeg", units = "in",
# width = 10, height = 6, res = 250)
corrplot(Var_corr$r, type = "upper",
method = "color", #title = "Pairwise Pearson r among variables",
tl.cex = 0.7, tl.col = 'black',#order = 'AOE',
p.mat = Var_corr$P,
sig.level = c(0.001, 0.01, 0.05), pch.cex = 0.6,
insig = 'label_sig', pch.col = 'black',
mar=c(1, 1, 1, 1))#dev.off()Selecting a random subsample of 135 models to validate
set.seed(1989)
subsample_models <- mediation_multiverse %>%
filter(Effect == "Indirect") %>% # we are looking to subsample only our 135 unique models, not all effect types
dplyr::select(X, Y, M) %>%
sample_n(tbl = .,
size = 6,
replace = FALSE)
write.csv(x = subsample_models,
file = "./subsample_N6_models.csv",
row.names = F)Saving dataset to validate models on, e.g., simply selecting primary variables from the 128 variable list. This data will be used for coauthor, FH, to validate the random subsample of 3
sub_data <- data %>%
# Covariates
dplyr::select(subjectkey, Age, sex_r, race_r,
# IVS
FamEnv_Fact, Demo_Fact, Child_Fact, Par_Fact, FES_youth, FES_parent,
PMON, CRPBI, Avg_IncomeEduc,
# Mediators
p_puberty, y_puberty,avg_puberty,
# Dependent variables
Amygdala_vol, ACC_CT, ACC_CA, L_AmygCing_rest, R_AmygCing_rest)
write.csv(x = sub_data,
file = "./sub_data.csv",
row.names = F)